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Break
Mon Jun 10th 08:45 -- 09:15 AM @ None
Coffee Break
Tutorial
Mon Jun 10th 09:15 -- 11:30 AM @ Hall A
Recent Advances in Population-Based Search for Deep Neural Networks: Quality Diversity, Indirect Encodings, and Open-Ended Algorithms
Jeff Clune · Joel Lehman · Kenneth Stanley
Tutorial
Mon Jun 10th 09:15 -- 11:30 AM @ Grand Ballroom
A Primer on PAC-Bayesian Learning
Benjamin Guedj · John Shawe-Taylor
Tutorial
Mon Jun 10th 09:15 -- 11:30 AM @ Room 104
Safe Machine Learning
Silvia Chiappa · Jan Leike
Tutorial
Mon Jun 10th 09:15 -- 11:30 AM @ Hall B
Never-Ending Learning
Tom Mitchell · Partha Talukdar
Break
Mon Jun 10th 11:30 AM -- 01:00 PM @ None
Lunch - on your own
Tutorial
Mon Jun 10th 01:00 -- 03:15 PM @ Hall B
Active Learning: From Theory to Practice
Robert Nowak · Steve Hanneke
Tutorial
Mon Jun 10th 01:00 -- 03:15 PM @ Grand Ballroom
Neural Approaches to Conversational AI
Michel Galley · Jianfeng Gao
Tutorial
Mon Jun 10th 01:00 -- 03:15 PM @ Hall A
Meta-Learning: from Few-Shot Learning to Rapid Reinforcement Learning
Chelsea Finn · Sergey Levine
Break
Mon Jun 10th 03:15 -- 03:45 PM @ None
Coffee Break
Tutorial
Mon Jun 10th 03:45 -- 06:00 PM @ Hall B
Active Hypothesis Testing: An Information Theoretic (re)View
Tara Javidi
Tutorial
Mon Jun 10th 03:45 -- 06:00 PM @ Grand Ballroom
Algorithm configuration: learning in the space of algorithm designs
Kevin Leyton-Brown · Frank Hutter
Tutorial
Mon Jun 10th 03:45 -- 06:00 PM @ Hall A
A Tutorial on Attention in Deep Learning
Alex Smola · Aston Zhang
Tutorial
Mon Jun 10th 03:45 -- 06:00 PM @ Room 104
Causal Inference and Stable Learning
Tong Zhang · Peng Cui
Break
Mon Jun 10th 06:00 -- 07:30 PM @ None
Opening Reception
Talk
Tue Jun 11th 08:45 -- 09:00 AM @ Hall A
Opening Remarks
Kamalika Chaudhuri · Ruslan Salakhutdinov
Invited Talk
Tue Jun 11th 09:00 -- 10:00 AM @ Hall A
Machine learning for robots to think fast
Aude Billard
Invited Talk
Tue Jun 11th 10:00 -- 10:20 AM @ Hall A
Best Paper
Oral
Tue Jun 11th 10:00 -- 10:20 AM @ Hall A
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
Francesco Locatello · Stefan Bauer · Mario Lucic · Gunnar Raetsch · Sylvain Gelly · Bernhard Schölkopf · Olivier Bachem
Break
Tue Jun 11th 10:30 -- 11:00 AM @ None
Coffee Break
Oral
Tue Jun 11th 11:00 -- 11:20 AM @ Room 101
A Contrastive Divergence for Combining Variational Inference and MCMC
Francisco Ruiz · Michalis Titsias
Oral
Tue Jun 11th 11:00 -- 11:20 AM @ Hall A
SelectiveNet: A Deep Neural Network with an Integrated Reject Option
Yonatan Geifman · Ran El-Yaniv
Oral
Tue Jun 11th 11:00 -- 11:20 AM @ Room 102
Regret Circuits: Composability of Regret Minimizers
Gabriele Farina · Christian Kroer · Tuomas Sandholm
Oral
Tue Jun 11th 11:00 -- 11:20 AM @ Room 103
Refined Complexity of PCA with Outliers
Kirill Simonov · Fedor Fomin · Petr Golovach · Fahad Panolan
Oral
Tue Jun 11th 11:00 -- 11:20 AM @ Grand Ballroom
Adversarial Attacks on Node Embeddings via Graph Poisoning
Aleksandar Bojchevski · Stephan Günnemann
Oral
Tue Jun 11th 11:00 -- 11:20 AM @ Room 201
Validating Causal Inference Models via Influence Functions
Ahmed Alaa · M van der Schaar
Oral
Tue Jun 11th 11:00 -- 11:20 AM @ Hall B
ELF OpenGo: an analysis and open reimplementation of AlphaZero
Yuandong Tian · Jerry Ma · Qucheng Gong · Shubho Sengupta · Zhuoyuan Chen · James Pinkerton · Larry Zitnick
Oral
Tue Jun 11th 11:00 -- 11:20 AM @ Seaside Ballroom
Data Shapley: Equitable Valuation of Data for Machine Learning
Amirata Ghorbani · James Zou
Oral
Tue Jun 11th 11:00 -- 11:20 AM @ Room 104
PA-GD: On the Convergence of Perturbed Alternating Gradient Descent to Second-Order Stationary Points for Structured Nonconvex Optimization
Songtao Lu · Mingyi Hong · Zhengdao Wang
Oral
Tue Jun 11th 11:20 -- 11:25 AM @ Seaside Ballroom
Feature Grouping as a Stochastic Regularizer for High-Dimensional Structured Data
Sergul Aydore · Thirion Bertrand · Gael Varoquaux
Oral
Tue Jun 11th 11:20 -- 11:25 AM @ Room 104
Improved Zeroth-Order Variance Reduced Algorithms and Analysis for Nonconvex Optimization
Kaiyi Ji · Zhe Wang · Yi Zhou · Yingbin LIANG
Oral
Tue Jun 11th 11:20 -- 11:25 AM @ Room 201
Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks
Charith Mendis · Alex Renda · Dr.Saman Amarasinghe · Michael Carbin
Oral
Tue Jun 11th 11:20 -- 11:25 AM @ Grand Ballroom
First-Order Adversarial Vulnerability of Neural Networks and Input Dimension
Carl-Johann Simon-Gabriel · Yann Ollivier · Leon Bottou · Bernhard Schölkopf · David Lopez-Paz
Oral
Tue Jun 11th 11:20 -- 11:25 AM @ Room 101
Calibrated Approximate Bayesian Inference
Hanwen Xing · Geoff Nicholls · Jeong Lee
Oral
Tue Jun 11th 11:20 -- 11:25 AM @ Room 102
Game Theoretic Optimization via Gradient-based Nikaido-Isoda Function
Arvind Raghunathan · Anoop Cherian · Devesh Jha
Oral
Tue Jun 11th 11:20 -- 11:25 AM @ Hall A
Manifold Mixup: Better Representations by Interpolating Hidden States
Vikas Verma · Alex Lamb · Christopher Beckham · Amir Najafi · Ioannis Mitliagkas · David Lopez-Paz · Yoshua Bengio
Oral
Tue Jun 11th 11:20 -- 11:25 AM @ Hall B
Making Deep Q-learning methods robust to time discretization
Corentin Tallec · Leonard Blier · Yann Ollivier
Oral
Tue Jun 11th 11:20 -- 11:25 AM @ Room 103
On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms
Tianyi Lin · Nhat Ho · Michael Jordan
Oral
Tue Jun 11th 11:25 -- 11:30 AM @ Room 103
Passed & Spurious: Descent Algorithms and Local Minima in Spiked Matrix-Tensor Models
Stefano Sarao Mannelli · Florent Krzakala · Pierfrancesco Urbani · Lenka Zdeborova
Oral
Tue Jun 11th 11:25 -- 11:30 AM @ Room 102
Stable-Predictive Optimistic Counterfactual Regret Minimization
Gabriele Farina · Christian Kroer · Noam Brown · Tuomas Sandholm
Oral
Tue Jun 11th 11:25 -- 11:30 AM @ Grand Ballroom
On Certifying Non-Uniform Bounds against Adversarial Attacks
Chen Liu · Ryota Tomioka · Volkan Cevher
Oral
Tue Jun 11th 11:25 -- 11:30 AM @ Room 104
Faster Stochastic Alternating Direction Method of Multipliers for Nonconvex Optimization
Feihu Huang · Songcan Chen · Heng Huang
Oral
Tue Jun 11th 11:25 -- 11:30 AM @ Seaside Ballroom
Metric-Optimized Example Weights
Sen Zhao · Mahdi Milani Fard · Harikrishna Narasimhan · Maya Gupta
Oral
Tue Jun 11th 11:25 -- 11:30 AM @ Hall B
Nonlinear Distributional Gradient Temporal-Difference Learning
chao qu · Shie Mannor · Huan Xu
Oral
Tue Jun 11th 11:25 -- 11:30 AM @ Hall A
Processing Megapixel Images with Deep Attention-Sampling Models
Angelos Katharopoulos · Francois Fleuret
Oral
Tue Jun 11th 11:25 -- 11:30 AM @ Room 101
Moment-Based Variational Inference for Markov Jump Processes
Christian Wildner · Heinz Koeppl
Oral
Tue Jun 11th 11:25 -- 11:30 AM @ Room 201
Learning to Groove with Inverse Sequence Transformations
Jon Gillick · Adam Roberts · Jesse Engel · Douglas Eck · David Bamman
Oral
Tue Jun 11th 11:30 -- 11:35 AM @ Room 201
Grid-Wise Control for Multi-Agent Reinforcement Learning in Video Game AI
Lei Han · Peng Sun · Yali Du · Jiechao Xiong · Qing Wang · Xinghai Sun · Han Liu · Tong Zhang
Oral
Tue Jun 11th 11:30 -- 11:35 AM @ Room 104
Lower Bounds for Smooth Nonconvex Finite-Sum Optimization
Dongruo Zhou · Quanquan Gu
Oral
Tue Jun 11th 11:30 -- 11:35 AM @ Grand Ballroom
Improving Adversarial Robustness via Promoting Ensemble Diversity
Tianyu Pang · Kun Xu · Chao Du · Ning Chen · Jun Zhu
Oral
Tue Jun 11th 11:30 -- 11:35 AM @ Room 103
Teaching a black-box learner
Sanjoy Dasgupta · Daniel Hsu · Stefanos Poulis · Jerry Zhu
Oral
Tue Jun 11th 11:30 -- 11:35 AM @ Room 102
When Samples Are Strategically Selected
Hanrui Zhang · Yu Cheng · Vincent Conitzer
Oral
Tue Jun 11th 11:30 -- 11:35 AM @ Hall A
TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning
Sung Whan Yoon · Jun Seo · Jaekyun Moon
Oral
Tue Jun 11th 11:30 -- 11:35 AM @ Hall B
Composing Entropic Policies using Divergence Correction
Jonathan Hunt · Andre Barreto · Timothy Lillicrap · Nicolas Heess
Oral
Tue Jun 11th 11:30 -- 11:35 AM @ Seaside Ballroom
Improving Model Selection by Employing the Test Data
Max Westphal · Werner Brannath
Oral
Tue Jun 11th 11:30 -- 11:35 AM @ Room 101
Understanding MCMC Dynamics as Flows on the Wasserstein Space
Chang Liu · Jingwei Zhuo · Jun Zhu
Oral
Tue Jun 11th 11:35 -- 11:40 AM @ Room 201
HOList: An Environment for Machine Learning of Higher Order Logic Theorem Proving
Kshitij Bansal · Sarah Loos · Markus Rabe · Christian Szegedy · Stewart Wilcox
Oral
Tue Jun 11th 11:35 -- 11:40 AM @ Room 101
LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations
Brian Trippe · Jonathan Huggins · Raj Agrawal · Tamara Broderick
Oral
Tue Jun 11th 11:35 -- 11:40 AM @ Hall B
TibGM: A Transferable and Information-Based Graphical Model Approach for Reinforcement Learning
Tameem Adel · Adrian Weller
Oral
Tue Jun 11th 11:35 -- 11:40 AM @ Room 103
PAC Learnability of Node Functions in Networked Dynamical Systems
Abhijin Adiga · Chris J Kuhlman · Madhav Marathe · S. S. Ravi · Anil Vullikanti
Oral
Tue Jun 11th 11:35 -- 11:40 AM @ Seaside Ballroom
Topological Data Analysis of Decision Boundaries with Application to Model Selection
Karthikeyan Ramamurthy · Kush Varshney · Krishnan Mody
Oral
Tue Jun 11th 11:35 -- 11:40 AM @ Room 104
Nonconvex Variance Reduced Optimization with Arbitrary Sampling
Samuel Horvath · Peter Richtarik
Oral
Tue Jun 11th 11:35 -- 11:40 AM @ Grand Ballroom
Adversarial camera stickers: A physical camera-based attack on deep learning systems
Juncheng Li · Frank Schmidt · Zico Kolter
Oral
Tue Jun 11th 11:35 -- 11:40 AM @ Room 102
Statistical Foundations of Virtual Democracy
Anson Kahng · Min Kyung Lee · Ritesh Noothigattu · Ariel Procaccia · Christos-Alexandros Psomas
Oral
Tue Jun 11th 11:35 -- 11:40 AM @ Hall A
Online Meta-Learning
Chelsea Finn · Aravind Rajeswaran · Sham Kakade · Sergey Levine
Oral
Tue Jun 11th 11:40 AM -- 12:00 PM @ Room 101
Amortized Monte Carlo Integration
Adam Golinski · Frank Wood · Tom Rainforth
Oral
Tue Jun 11th 11:40 AM -- 12:00 PM @ Hall A
Training Neural Networks with Local Error Signals
Arild Nøkland · Lars Hiller Eidnes
Oral
Tue Jun 11th 11:40 AM -- 12:00 PM @ Room 201
Molecular Hypergraph Grammar with Its Application to Molecular Optimization
Hiroshi Kajino
Oral
Tue Jun 11th 11:40 AM -- 12:00 PM @ Room 102
Optimal Auctions through Deep Learning
Paul Duetting · Zhe Feng · Harikrishna Narasimhan · David Parkes · Sai Srivatsa Ravindranath
Oral
Tue Jun 11th 11:40 AM -- 12:00 PM @ Room 103
Online learning with kernel losses
Niladri S Chatterji · Aldo Pacchiano · Peter Bartlett
Oral
Tue Jun 11th 11:40 AM -- 12:00 PM @ Hall B
Multi-Agent Adversarial Inverse Reinforcement Learning
Lantao Yu · Jiaming Song · Stefano Ermon
Oral
Tue Jun 11th 11:40 AM -- 12:00 PM @ Seaside Ballroom
Contextual Memory Trees
Wen Sun · Alina Beygelzimer · Hal Daume · John Langford · Paul Mineiro
Oral
Tue Jun 11th 11:40 AM -- 12:00 PM @ Room 104
Error Feedback Fixes SignSGD and other Gradient Compression Schemes
Sai Praneeth Reddy Karimireddy · Quentin Rebjock · Sebastian Stich · Martin Jaggi
Oral
Tue Jun 11th 11:40 AM -- 12:00 PM @ Grand Ballroom
Adversarial examples from computational constraints
Sebastien Bubeck · Yin Tat Lee · Eric Price · Ilya Razenshteyn
Oral
Tue Jun 11th 12:00 -- 12:05 PM @ Hall B
Policy Consolidation for Continual Reinforcement Learning
Christos Kaplanis · Murray Shanahan · Claudia Clopath
Oral
Tue Jun 11th 12:00 -- 12:05 PM @ Room 103
Nearest Neighbor and Kernel Survival Analysis: Nonasymptotic Error Bounds and Strong Consistency Rates
George Chen
Oral
Tue Jun 11th 12:00 -- 12:05 PM @ Room 101
Stein Point Markov Chain Monte Carlo
Wilson Ye Chen · Alessandro Barp · Francois-Xavier Briol · Jackson Gorham · Mark Girolami · Lester Mackey · Chris Oates
Oral
Tue Jun 11th 12:00 -- 12:05 PM @ Seaside Ballroom
Sparse Extreme Multi-label Learning with Oracle Property
Weiwei Liu · Xiaobo Shen
Oral
Tue Jun 11th 12:00 -- 12:05 PM @ Room 201
Graph Neural Network for Music Score Data and Modeling Expressive Piano Performance
Dasaem Jeong · Taegyun Kwon · Yoojin Kim · Juhan Nam
Oral
Tue Jun 11th 12:00 -- 12:05 PM @ Room 102
Learning to Clear the Market
Weiran Shen · Sébastien Lahaie · Renato Leme
Oral
Tue Jun 11th 12:00 -- 12:05 PM @ Grand Ballroom
POPQORN: Quantifying Robustness of Recurrent Neural Networks
CHING-YUN KO · Zhaoyang Lyu · Tsui-Wei Weng · Luca Daniel · Ngai Wong · Dahua Lin
Oral
Tue Jun 11th 12:00 -- 12:05 PM @ Room 104
A Composite Randomized Incremental Gradient Method
Junyu Zhang · Lin Xiao
Oral
Tue Jun 11th 12:00 -- 12:05 PM @ Hall A
GMNN: Graph Markov Neural Networks
Meng Qu · Yoshua Bengio · Jian Tang
Oral
Tue Jun 11th 12:05 -- 12:10 PM @ Room 201
Learning to Prove Theorems via Interacting with Proof Assistants
Kaiyu Yang · Jia Deng
Oral
Tue Jun 11th 12:05 -- 12:10 PM @ Hall A
Self-Attention Graph Pooling
Junhyun Lee · Inyeop Lee · Jaewoo Kang
Oral
Tue Jun 11th 12:05 -- 12:10 PM @ Hall B
Off-Policy Deep Reinforcement Learning without Exploration
Scott Fujimoto · David Meger · Doina Precup
Oral
Tue Jun 11th 12:05 -- 12:10 PM @ Room 103
Fast Rates for a kNN Classifier Robust to Unknown Asymmetric Label Noise
Henry Reeve · Ata Kaban
Oral
Tue Jun 11th 12:05 -- 12:10 PM @ Grand Ballroom
Using Pre-Training Can Improve Model Robustness and Uncertainty
Dan Hendrycks · Kimin Lee · Mantas Mazeika
Oral
Tue Jun 11th 12:05 -- 12:10 PM @ Room 101
Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations
Wu Lin · Mohammad Emtiyaz Khan · Mark Schmidt
Oral
Tue Jun 11th 12:05 -- 12:10 PM @ Room 102
Learning to bid in revenue-maximizing auctions
Thomas Nedelec · Noureddine El Karoui · Vianney Perchet
Oral
Tue Jun 11th 12:05 -- 12:10 PM @ Room 104
Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field Inference
Yatao (An) Bian · Joachim Buhmann · Andreas Krause
Oral
Tue Jun 11th 12:05 -- 12:10 PM @ Seaside Ballroom
Shape Constraints for Set Functions
Andrew Cotter · Maya Gupta · Heinrich Jiang · Erez Louidor · James Muller · Tamann Narayan · Serena Wang · Tao Zhu
Oral
Tue Jun 11th 12:10 -- 12:15 PM @ Room 104
Multiplicative Weights Updates as a distributed constrained optimization algorithm: Convergence to second-order stationary points almost always
Ioannis Panageas · Georgios Piliouras · xiao wang
Oral
Tue Jun 11th 12:10 -- 12:15 PM @ Seaside Ballroom
On The Power of Curriculum Learning in Training Deep Networks
Guy Hacohen · Daphna Weinshall
Oral
Tue Jun 11th 12:10 -- 12:15 PM @ Room 103
Uniform Convergence Rate of the Kernel Density Estimator Adaptive to Intrinsic Volume Dimension
Jisu Kim · Jaehyeok Shin · Alessandro Rinaldo · Larry Wasserman
Oral
Tue Jun 11th 12:10 -- 12:15 PM @ Hall B
Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation
Ruohan Wang · Carlo Ciliberto · Pierluigi Vito Amadori · Yiannis Demiris
Oral
Tue Jun 11th 12:10 -- 12:15 PM @ Room 101
Particle Flow Bayes' Rule
Xinshi Chen · Hanjun Dai · Le Song
Oral
Tue Jun 11th 12:10 -- 12:15 PM @ Room 102
Open-ended learning in symmetric zero-sum games
David Balduzzi · Marta Garnelo · Yoram Bachrach · Wojciech Czarnecki · Julien Perolat · Max Jaderberg · Thore Graepel
Oral
Tue Jun 11th 12:10 -- 12:15 PM @ Hall A
Combating Label Noise in Deep Learning using Abstention
Sunil Thulasidasan · Tanmoy Bhattacharya · Jeff Bilmes · Gopinath Chennupati · Jamal Mohd-Yusof
Oral
Tue Jun 11th 12:10 -- 12:15 PM @ Grand Ballroom
Generalized No Free Lunch Theorem for Adversarial Robustness
Elvis Dohmatob
Oral
Tue Jun 11th 12:10 -- 12:15 PM @ Room 201
Circuit-GNN: Graph Neural Networks for Distributed Circuit Design
GUO ZHANG · Hao He · Dina Katabi
Oral
Tue Jun 11th 12:15 -- 12:20 PM @ Room 104
Katalyst: Boosting Convex Katayusha for Non-Convex Problems with a Large Condition Number
Zaiyi Chen · Yi Xu · Haoyuan Hu · Tianbao Yang
Oral
Tue Jun 11th 12:15 -- 12:20 PM @ Room 101
Correlated Variational Auto-Encoders
Da Tang · Dawen Liang · Tony Jebara · Nicholas Ruozzi
Oral
Tue Jun 11th 12:15 -- 12:20 PM @ Hall B
Revisiting the Softmax Bellman Operator: New Benefits and New Perspective
Zhao Song · Ron Parr · Lawrence Carin
Oral
Tue Jun 11th 12:15 -- 12:20 PM @ Room 201
Learning to Optimize Multigrid PDE Solvers
Daniel Greenfeld · Meirav Galun · Ronen Basri · Irad Yavneh · Ron Kimmel
Oral
Tue Jun 11th 12:15 -- 12:20 PM @ Room 103
Maximum Likelihood Estimation for Learning Populations of Parameters
Ramya Korlakai Vinayak · Weihao Kong · Gregory Valiant · Sham Kakade
Oral
Tue Jun 11th 12:15 -- 12:20 PM @ Grand Ballroom
PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach
Tsui-Wei Weng · Pin-Yu Chen · Lam Nguyen · Mark Squillante · Akhilan Boopathy · Ivan Oseledets · Luca Daniel
Oral
Tue Jun 11th 12:15 -- 12:20 PM @ Room 102
Deep Counterfactual Regret Minimization
Noam Brown · Adam Lerer · Sam Gross · Tuomas Sandholm
Oral
Tue Jun 11th 12:15 -- 12:20 PM @ Hall A
LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning
Huaiyu Li · Weiming Dong · Xing Mei · Chongyang Ma · Feiyue Huang · Bao-Gang Hu
Oral
Tue Jun 11th 12:15 -- 12:20 PM @ Seaside Ballroom
Voronoi Boundary Classification: A High-Dimensional Geometric Approach via Weighted Monte Carlo Integration
Vladislav Polianskii · Florian Pokorny
Break
Tue Jun 11th 12:30 -- 02:00 PM @ None
Lunch - on your own
Oral
Tue Jun 11th 02:00 -- 02:20 PM @ Room 103
Projection onto Minkowski Sums with Application to Constrained Learning
Joong-Ho Won · Jason Xu · Kenneth Lange
Oral
Tue Jun 11th 02:00 -- 02:20 PM @ Hall B
An Investigation of Model-Free Planning
Arthur Guez · Mehdi Mirza · Karol Gregor · Rishabh Kabra · Sebastien Racaniere · Theophane Weber · David Raposo · Adam Santoro · Laurent Orseau · Tom Eccles · Greg Wayne · David Silver · Timothy Lillicrap
Oral
Tue Jun 11th 02:00 -- 02:20 PM @ Grand Ballroom
On Learning Invariant Representations for Domain Adaptation
Han Zhao · Remi Tachet des Combes · Kun Zhang · Geoff Gordon
Oral
Tue Jun 11th 02:00 -- 02:20 PM @ Room 201
A Block Coordinate Descent Proximal Method for Simultaneous Filtering and Parameter Estimation
Ramin Raziperchikolaei · Harish Bhat
Oral
Tue Jun 11th 02:00 -- 02:20 PM @ Room 104
Safe Policy Improvement with Baseline Bootstrapping
Romain Laroche · Paul TRICHELAIR · Remi Tachet des Combes
Oral
Tue Jun 11th 02:00 -- 02:20 PM @ Seaside Ballroom
Robust Decision Trees Against Adversarial Examples
Hongge Chen · Huan Zhang · Duane Boning · Cho-Jui Hsieh
Oral
Tue Jun 11th 02:00 -- 02:20 PM @ Room 102
Generalized Approximate Survey Propagation for High-Dimensional Estimation
Carlo Lucibello · Luca Saglietti · Yue Lu
Oral
Tue Jun 11th 02:00 -- 02:20 PM @ Hall A
Self-Attention Generative Adversarial Networks
Han Zhang · Ian Goodfellow · Dimitris Metaxas · Augustus Odena
Oral
Tue Jun 11th 02:00 -- 02:20 PM @ Room 101
Towards a Unified Analysis of Random Fourier Features
Zhu Li · Jean-Francois Ton · Dino Oglic · Dino Sejdinovic
Oral
Tue Jun 11th 02:20 -- 02:25 PM @ Room 201
Learning Hawkes Processes Under Synchronization Noise
William Trouleau · Jalal Etesami · Matthias Grossglauser · Negar Kiyavash · Patrick Thiran
Oral
Tue Jun 11th 02:20 -- 02:25 PM @ Hall A
Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution Matching
Ziliang Chen · ZHANFU YANG · Xiaoxi Wang · Xiaodan Liang · xiaopeng yan · Guanbin Li · Liang Lin
Oral
Tue Jun 11th 02:20 -- 02:25 PM @ Room 104
Distributional Reinforcement Learning for Efficient Exploration
Borislav Mavrin · Hengshuai Yao · Linglong Kong · Kaiwen Wu · Yaoliang Yu
Oral
Tue Jun 11th 02:20 -- 02:25 PM @ Room 103
Blended Conditonal Gradients
Gábor Braun · Sebastian Pokutta · Dan Tu · Stephen Wright
Oral
Tue Jun 11th 02:20 -- 02:25 PM @ Room 101
Learning deep kernels for exponential family densities
Li Kevin Wenliang · Dougal Sutherland · Heiko Strathmann · Arthur Gretton
Oral
Tue Jun 11th 02:20 -- 02:25 PM @ Hall B
CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning
Cédric Colas · Pierre-Yves Oudeyer · Olivier Sigaud · Pierre Fournier · Mohamed Chetouani
Oral
Tue Jun 11th 02:20 -- 02:25 PM @ Room 102
Boosted Density Estimation Remastered
Zac Cranko · Richard Nock
Oral
Tue Jun 11th 02:20 -- 02:25 PM @ Grand Ballroom
Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models
Mor Shpigel Nacson · Suriya Gunasekar · Jason Lee · Nati Srebro · Daniel Soudry
Oral
Tue Jun 11th 02:20 -- 02:25 PM @ Seaside Ballroom
Automatic Classifiers as Scientific Instruments: One Step Further Away from Ground-Truth
Jacob Whitehill · Anand Ramakrishnan
Oral
Tue Jun 11th 02:25 -- 02:30 PM @ Room 103
Acceleration of SVRG and Katyusha X by Inexact Preconditioning
Yanli Liu · Fei Feng · Wotao Yin
Oral
Tue Jun 11th 02:25 -- 02:30 PM @ Grand Ballroom
Adversarial Generation of Time-Frequency Features with application in audio synthesis
Andrés Marafioti · Nathanaël Perraudin · Nicki Holighaus · Piotr Majdak
Oral
Tue Jun 11th 02:25 -- 02:30 PM @ Room 102
Inference and Sampling of $K_{33}$-free Ising Models
Valerii Likhosherstov · Yury Maximov · Misha Chertkov
Oral
Tue Jun 11th 02:25 -- 02:30 PM @ Room 104
Optimistic Policy Optimization via Multiple Importance Sampling
Matteo Papini · Alberto Maria Metelli · Lorenzo Lupo · Marcello Restelli
Oral
Tue Jun 11th 02:25 -- 02:30 PM @ Seaside Ballroom
Look Ma, No Latent Variables: Accurate Cutset Networks via Compilation
Tahrima Rahman · Shasha Jin · Vibhav Gogate
Oral
Tue Jun 11th 02:25 -- 02:30 PM @ Room 201
Generative Adversarial User Model for Reinforcement Learning Based Recommendation System
Xinshi Chen · Shuang Li · Hui Li · Shaohua Jiang · Yuan Qi · Le Song
Oral
Tue Jun 11th 02:25 -- 02:30 PM @ Hall A
High-Fidelity Image Generation With Fewer Labels
Mario Lucic · Michael Tschannen · Marvin Ritter · Xiaohua Zhai · Olivier Bachem · Sylvain Gelly
Oral
Tue Jun 11th 02:25 -- 02:30 PM @ Room 101
Bayesian Deconditional Kernel Mean Embeddings
Kelvin Hsu · Fabio Ramos
Oral
Tue Jun 11th 02:25 -- 02:30 PM @ Hall B
Task-Agnostic Dynamics Priors for Deep Reinforcement Learning
Yilun Du · Karthik Narasimhan
Oral
Tue Jun 11th 02:30 -- 02:35 PM @ Room 104
Neural Logic Reinforcement Learning
zhengyao jiang · Shan Luo
Oral
Tue Jun 11th 02:30 -- 02:35 PM @ Room 201
A Statistical Investigation of Long Memory in Language and Music
Alexander Greaves-Tunnell · Zaid Harchaoui
Oral
Tue Jun 11th 02:30 -- 02:35 PM @ Room 102
Random Matrix Improved Covariance Estimation for a Large Class of Metrics
Malik TIOMOKO A · Romain Couillet · Florent BOUCHARD · Guillaume GINOLHAC
Oral
Tue Jun 11th 02:30 -- 02:35 PM @ Hall A
Revisiting precision recall definition for generative modeling
Loic Simon · Ryan Webster · Julien Rabin
Oral
Tue Jun 11th 02:30 -- 02:35 PM @ Grand Ballroom
On the Universality of Invariant Networks
Haggai Maron · Ethan Fetaya · Nimrod Segol · Yaron Lipman
Oral
Tue Jun 11th 02:30 -- 02:35 PM @ Room 101
A Kernel Perspective for Regularizing Deep Neural Networks
Alberto Bietti · Gregoire Mialon · Dexiong Chen · Julien Mairal
Oral
Tue Jun 11th 02:30 -- 02:35 PM @ Hall B
Diagnosing Bottlenecks in Deep Q-learning Algorithms
Justin Fu · Aviral Kumar · Matthew Soh · Sergey Levine
Oral
Tue Jun 11th 02:30 -- 02:35 PM @ Seaside Ballroom
Optimal Transport for structured data with application on graphs
Titouan Vayer · Nicolas Courty · Romain Tavenard · Chapel Laetitia · Remi Flamary
Oral
Tue Jun 11th 02:30 -- 02:35 PM @ Room 103
Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD
Marten van Dijk · Lam Nguyen · PHUONG_HA NGUYEN · Dzung Phan
Oral
Tue Jun 11th 02:35 -- 02:40 PM @ Hall B
Collaborative Evolutionary Reinforcement Learning
Shauharda Khadka · Somdeb Majumdar · Tarek Nassar · Zach Dwiel · Evren Tumer · Santiago Miret · Yinyin Liu · Kagan Tumer
Oral
Tue Jun 11th 02:35 -- 02:40 PM @ Room 101
A Persistent Weisfeiler--Lehman Procedure for Graph Classification
Bastian Rieck · Christian Bock · Karsten Borgwardt
Oral
Tue Jun 11th 02:35 -- 02:40 PM @ Hall A
Wasserstein of Wasserstein Loss for Learning Generative Models
Yonatan Dukler · Wuchen Li · Alex Lin · Guido Montufar
Oral
Tue Jun 11th 02:35 -- 02:40 PM @ Room 103
A Conditional-Gradient-Based Augmented Lagrangian Framework
Alp Yurtsever · Olivier Fercoq · Volkan Cevher
Oral
Tue Jun 11th 02:35 -- 02:40 PM @ Seaside Ballroom
Learning Optimal Linear Regularizers
Matthew Streeter
Oral
Tue Jun 11th 02:35 -- 02:40 PM @ Room 104
Learning to Collaborate in Markov Decision Processes
Goran Radanovic · Rati Devidze · David Parkes · Adish Singla
Oral
Tue Jun 11th 02:35 -- 02:40 PM @ Room 102
Dual Entangled Polynomial Code: Three-Dimensional Coding for Distributed Matrix Multiplication
Pedro Soto · Jun Li · Xiaodi Fan
Oral
Tue Jun 11th 02:35 -- 02:40 PM @ Grand Ballroom
Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks
Sanjeev Arora · Simon Du · Wei Hu · Zhiyuan Li · Ruosong Wang
Oral
Tue Jun 11th 02:35 -- 02:40 PM @ Room 201
Deep Factors for Forecasting
Yuyang Wang · Alex Smola · Danielle Robinson · Jan Gasthaus · Dean Foster · Tim Januschowski
Oral
Tue Jun 11th 02:40 -- 03:00 PM @ Hall B
EMI: Exploration with Mutual Information
Hyoungseok Kim · Jaekyeom Kim · Yeonwoo Jeong · Sergey Levine · Hyun Oh Song
Oral
Tue Jun 11th 02:40 -- 03:00 PM @ Room 101
Rehashing Kernel Evaluation in High Dimensions
Paris Siminelakis · Kexin Rong · Peter Bailis · Moses Charikar · Philip Levis
Oral
Tue Jun 11th 02:40 -- 03:00 PM @ Hall A
Flat Metric Minimization with Applications in Generative Modeling
Thomas Möllenhoff · Daniel Cremers
Oral
Tue Jun 11th 02:40 -- 03:00 PM @ Room 201
Weakly-Supervised Temporal Localization via Occurrence Count Learning
Julien Schroeter · Kirill Sidorov · David Marshall
Oral
Tue Jun 11th 02:40 -- 03:00 PM @ Seaside Ballroom
On Symmetric Losses for Learning from Corrupted Labels
Nontawat Charoenphakdee · Jongyeong Lee · Masashi Sugiyama
Oral
Tue Jun 11th 02:40 -- 03:00 PM @ Room 103
SGD: General Analysis and Improved Rates
Robert M. Gower · Nicolas Loizou · Xun Qian · Alibek Sailanbayev · Egor Shulgin · Peter Richtarik
Oral
Tue Jun 11th 02:40 -- 03:00 PM @ Room 102
Neural Joint Source-Channel Coding
Kristy Choi · Kedar Tatwawadi · Aditya Grover · Tsachy Weissman · Stefano Ermon
Oral
Tue Jun 11th 02:40 -- 03:00 PM @ Grand Ballroom
Gauge Equivariant Convolutional Networks and the Icosahedral CNN
Taco Cohen · Maurice Weiler · Berkay Kicanaoglu · Max Welling
Oral
Tue Jun 11th 02:40 -- 03:00 PM @ Room 104
Predictor-Corrector Policy Optimization
Ching-An Cheng · Xinyan Yan · Nathan Ratliff · Byron Boots
Oral
Tue Jun 11th 03:00 -- 03:05 PM @ Seaside Ballroom
AUCµ: A Performance Metric for Multi-Class Machine Learning Models
Ross Kleiman · University of Wisconsin David Page
Oral
Tue Jun 11th 03:00 -- 03:05 PM @ Room 201
Switching Linear Dynamics for Variational Bayes Filtering
Philip Becker-Ehmck · Jan Peters · Patrick van der Smagt
Oral
Tue Jun 11th 03:00 -- 03:05 PM @ Hall B
Imitation Learning from Imperfect Demonstration
Yueh-Hua Wu · Nontawat Charoenphakdee · Han Bao · Voot Tangkaratt · Masashi Sugiyama
Oral
Tue Jun 11th 03:00 -- 03:05 PM @ Room 103
Curvature-Exploiting Acceleration of Elastic Net Computations
Vien Van Mai · Mikael Johansson
Oral
Tue Jun 11th 03:00 -- 03:05 PM @ Room 101
Large-Scale Sparse Kernel Canonical Correlation Analysis
Viivi Uurtio · Sahely Bhadra · Juho Rousu
Oral
Tue Jun 11th 03:00 -- 03:05 PM @ Room 104
Learning a Prior over Intent via Meta-Inverse Reinforcement Learning
Kelvin Xu · Ellis Ratner · EECS Anca Dragan · Sergey Levine · Chelsea Finn
Oral
Tue Jun 11th 03:00 -- 03:05 PM @ Hall A
Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs
Yogesh Balaji · Hamed Hassani · Rama Chellappa · Soheil Feizi
Oral
Tue Jun 11th 03:00 -- 03:05 PM @ Room 102
Doubly-Competitive Distribution Estimation
Yi Hao · Alon Orlitsky
Oral
Tue Jun 11th 03:00 -- 03:05 PM @ Grand Ballroom
Feature-Critic Networks for Heterogeneous Domain Generalization
Yiying Li · Yongxin Yang · Wei Zhou · Timothy Hospedales
Oral
Tue Jun 11th 03:05 -- 03:10 PM @ Room 201
Imputing Missing Events in Continuous-Time Event Streams
Hongyuan Mei · Guanghui Qin · Jason Eisner
Oral
Tue Jun 11th 03:05 -- 03:10 PM @ Hall A
Non-Parametric Priors For Generative Adversarial Networks
Rajhans Singh · Pavan Turaga · Suren Jayasuriya · Ravi Garg · Martin Braun
Oral
Tue Jun 11th 03:05 -- 03:10 PM @ Room 103
Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication
Anastasiia Koloskova · Sebastian Stich · Martin Jaggi
Oral
Tue Jun 11th 03:05 -- 03:10 PM @ Seaside Ballroom
Regularization in directable environments with application to Tetris
Jan Malte Lichtenberg · Ozgur Simsek
Oral
Tue Jun 11th 03:05 -- 03:10 PM @ Grand Ballroom
Learning to Convolve: A Generalized Weight-Tying Approach
Nichita Diaconu · Daniel E Worrall
Oral
Tue Jun 11th 03:05 -- 03:10 PM @ Hall B
Curiosity-Bottleneck: Exploration By Distilling Task-Specific Novelty
Youngjin Kim · Daniel Nam · Hyunwoo Kim · Ji-Hoon Kim · Gunhee Kim
Oral
Tue Jun 11th 03:05 -- 03:10 PM @ Room 102
Homomorphic Sensing
Manolis Tsakiris · Liangzu Peng
Oral
Tue Jun 11th 03:05 -- 03:10 PM @ Room 101
A Kernel Theory of Modern Data Augmentation
Tri Dao · Albert Gu · Alexander J Ratner · Virginia Smith · Christopher De Sa · Christopher Re
Oral
Tue Jun 11th 03:05 -- 03:10 PM @ Room 104
DeepMDP: Learning Continuous Latent Space Models for Representation Learning
Carles Gelada · Saurabh Kumar · Jacob Buckman · Ofir Nachum · Marc Bellemare
Oral
Tue Jun 11th 03:10 -- 03:15 PM @ Room 103
Safe Grid Search with Optimal Complexity
Eugene Ndiaye · Tam Le · Olivier Fercoq · Joseph Salmon · Ichiro Takeuchi
Oral
Tue Jun 11th 03:10 -- 03:15 PM @ Hall A
Lipschitz Generative Adversarial Nets
Zhiming Zhou · Jiadong Liang · Yuxuan Song · Lantao Yu · Hongwei Wang · Weinan Zhang · Yong Yu · Zhihua Zhang
Oral
Tue Jun 11th 03:10 -- 03:15 PM @ Room 104
Importance Sampling Policy Evaluation with an Estimated Behavior Policy
Josiah Hanna · Scott Niekum · Peter Stone
Oral
Tue Jun 11th 03:10 -- 03:15 PM @ Seaside Ballroom
Improved Dynamic Graph Learning through Fault-Tolerant Sparsification
Chunjiang Zhu · Sabine Storandt · Kam-Yiu Lam · Song Han · Jinbo Bi
Oral
Tue Jun 11th 03:10 -- 03:15 PM @ Room 102
Phaseless PCA: Low-Rank Matrix Recovery from Column-wise Phaseless Measurements
Seyedehsara Nayer · Praneeth Narayanamurthy · Namrata Vaswani
Oral
Tue Jun 11th 03:10 -- 03:15 PM @ Grand Ballroom
On Dropout and Nuclear Norm Regularization
Poorya Mianjy · Raman Arora
Oral
Tue Jun 11th 03:10 -- 03:15 PM @ Room 101
kernelPSI: a Post-Selection Inference Framework for Nonlinear Variable Selection
Lotfi Slim · Clément Chatelain · Chloe-Agathe Azencott · Jean-Philippe Vert
Oral
Tue Jun 11th 03:10 -- 03:15 PM @ Room 201
Understanding and Controlling Memory in Recurrent Neural Networks
Doron Haviv · Alexander Rivkind · Omri Barak
Oral
Tue Jun 11th 03:10 -- 03:15 PM @ Hall B
Dynamic Weights in Multi-Objective Deep Reinforcement Learning
Axel Abels · Diederik Roijers · Tom Lenaerts · Ann Nowé · Denis Steckelmacher
Oral
Tue Jun 11th 03:15 -- 03:20 PM @ Hall A
HexaGAN: Generative Adversarial Nets for Real World Classification
Uiwon Hwang · Dahuin Jung · Sungroh Yoon
Oral
Tue Jun 11th 03:15 -- 03:20 PM @ Room 101
Scalable Learning in Reproducing Kernel Krein Spaces
Dino Oglic · Thomas Gaertner
Oral
Tue Jun 11th 03:15 -- 03:20 PM @ Grand Ballroom
Gradient Descent Finds Global Minima of Deep Neural Networks
Simon Du · Jason Lee · Haochuan Li · Liwei Wang · Xiyu Zhai
Oral
Tue Jun 11th 03:15 -- 03:20 PM @ Room 102
Rate Distortion For Model Compression:From Theory To Practice
Weihao Gao · Yu-Han Liu · Chong Wang · Sewoong Oh
Oral
Tue Jun 11th 03:15 -- 03:20 PM @ Room 104
Learning from a Learner
alexis jacq · Matthieu Geist · Ana Paiva · Olivier Pietquin
Oral
Tue Jun 11th 03:15 -- 03:20 PM @ Seaside Ballroom
Heterogeneous Model Reuse via Optimizing Multiparty Multiclass Margin
Xi-Zhu Wu · Song Liu · Zhi-Hua Zhou
Oral
Tue Jun 11th 03:15 -- 03:20 PM @ Room 201
Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces
Philipp Becker · Harit Pandya · Gregor Gebhardt · Cheng Zhao · C. James Taylor · Gerhard Neumann
Oral
Tue Jun 11th 03:15 -- 03:20 PM @ Hall B
Fingerprint Policy Optimisation for Robust Reinforcement Learning
Supratik Paul · Michael A Osborne · Shimon Whiteson
Oral
Tue Jun 11th 03:15 -- 03:20 PM @ Room 103
SAGA with Arbitrary Sampling
Xun Qian · Zheng Qu · Peter Richtarik
Break
Tue Jun 11th 03:30 -- 04:00 PM @ None
Coffee break
Oral
Tue Jun 11th 04:00 -- 04:20 PM @ Room 101
Dirichlet Simplex Nest and Geometric Inference
Mikhail Yurochkin · Aritra Guha · Yuekai Sun · XuanLong Nguyen
Oral
Tue Jun 11th 04:00 -- 04:20 PM @ Room 104
Separable value functions across time-scales
Joshua Romoff · Peter Henderson · Ahmed Touati · Yann Ollivier · Joelle Pineau · Emma Brunskill
Oral
Tue Jun 11th 04:00 -- 04:20 PM @ Hall B
An Investigation into Neural Net Optimization via Hessian Eigenvalue Density
Behrooz Ghorbani · Shankar Krishnan · Ying Xiao
Oral
Tue Jun 11th 04:00 -- 04:20 PM @ Room 201
Subspace Robust Wasserstein Distances
François-Pierre Paty · Marco Cuturi
Oral
Tue Jun 11th 04:00 -- 04:20 PM @ Grand Ballroom
Composable Core-sets for Determinant Maximization: A Simple Near-Optimal Algorithm
Sepideh Mahabadi · Piotr Indyk · Shayan Oveis Gharan · Alireza Rezaei
Oral
Tue Jun 11th 04:00 -- 04:20 PM @ Room 103
Natural Analysts in Adaptive Data Analysis
Tijana Zrnic · University of California Moritz Hardt
Oral
Tue Jun 11th 04:00 -- 04:20 PM @ Hall A
Graph Matching Networks for Learning the Similarity of Graph Structured Objects
Yujia Li · Chenjie Gu · Thomas Dullien · Oriol Vinyals · Pushmeet Kohli
Oral
Tue Jun 11th 04:00 -- 04:20 PM @ Room 102
Formal Privacy for Functional Data with Gaussian Perturbations
Ardalan Mirshani · Matthew Reimherr · Aleksandra Slavković
Oral
Tue Jun 11th 04:00 -- 04:20 PM @ Seaside Ballroom
Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff
Yochai Blau · Tomer Michaeli
Oral
Tue Jun 11th 04:20 -- 04:25 PM @ Room 102
Graphical-model based estimation and inference for differential privacy
Ryan McKenna · Daniel Sheldon · Gerome Miklau
Oral
Tue Jun 11th 04:20 -- 04:25 PM @ Room 104
Learning Action Representations for Reinforcement Learning
Yash Chandak · Georgios Theocharous · James Kostas · Scott Jordan · Philip Thomas
Oral
Tue Jun 11th 04:20 -- 04:25 PM @ Grand Ballroom
Sublinear Time Nearest Neighbor Search over Generalized Weighted Space
Yifan Lei · Qiang Huang · Mohan Kankanhalli · Anthony Tung
Oral
Tue Jun 11th 04:20 -- 04:25 PM @ Seaside Ballroom
Collaborative Channel Pruning for Deep Networks
Hanyu Peng · Jiaxiang Wu · Shifeng Chen · Junzhou Huang
Oral
Tue Jun 11th 04:20 -- 04:25 PM @ Room 201
Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models
Kaspar Märtens · Kieran Campbell · Christopher Yau
Oral
Tue Jun 11th 04:20 -- 04:25 PM @ Room 101
Bayesian leave-one-out cross-validation for large data
Måns Magnusson · Michael Andersen · Johan Jonasson · Aki Vehtari
Oral
Tue Jun 11th 04:20 -- 04:25 PM @ Hall B
Differentiable Linearized ADMM
Xingyu Xie · Jianlong Wu · Guangcan Liu · Zhisheng Zhong · Zhouchen Lin
Oral
Tue Jun 11th 04:20 -- 04:25 PM @ Room 103
CapsAndRuns: An Improved Method for Approximately Optimal Algorithm Configuration
Gellért Weisz · Andras Gyorgy · Csaba Szepesvari
Oral
Tue Jun 11th 04:20 -- 04:25 PM @ Hall A
BayesNAS: A Bayesian Approach for Neural Architecture Search
Hongpeng Zhou · Minghao Yang · Jun Wang · Wei Pan
Oral
Tue Jun 11th 04:25 -- 04:30 PM @ Room 104
Bayesian Counterfactual Risk Minimization
Ben London · Ted Sandler
Oral
Tue Jun 11th 04:25 -- 04:30 PM @ Room 101
Rao-Blackwellized Stochastic Gradients for Discrete Distributions
Runjing Liu · Jeffrey Regier · Nilesh Tripuraneni · Michael Jordan · Jon McAuliffe
Oral
Tue Jun 11th 04:25 -- 04:30 PM @ Room 201
Active Manifolds: A non-linear analogue to Active Subspaces
Robert Bridges · Anthony Gruber · Christopher Felder · Miki Verma · Chelsey Hoff
Oral
Tue Jun 11th 04:25 -- 04:30 PM @ Room 103
Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction
Giulia Luise · Dimitrios Stamos · Massimiliano Pontil · Carlo Ciliberto
Oral
Tue Jun 11th 04:25 -- 04:30 PM @ Grand Ballroom
Compressing Gradient Optimizers via Count-Sketches
Ryan Spring · Anastasios Kyrillidis · Vijai Mohan · Anshumali Shrivastava
Oral
Tue Jun 11th 04:25 -- 04:30 PM @ Seaside Ballroom
Same, Same But Different: Recovering Neural Network Quantization Error Through Weight Factorization
Eldad Meller · Alexander Finkelstein · Uri Almog · Mark Grobman
Oral
Tue Jun 11th 04:25 -- 04:30 PM @ Room 102
White-box vs Black-box: Bayes Optimal Strategies for Membership Inference
Alexandre Sablayrolles · Douze Matthijs · Cordelia Schmid · Yann Ollivier · Herve Jegou
Oral
Tue Jun 11th 04:25 -- 04:30 PM @ Hall A
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks
Juho Lee · Yoonho Lee · Jungtaek Kim · Adam Kosiorek · Seungjin Choi · Yee Whye Teh
Oral
Tue Jun 11th 04:25 -- 04:30 PM @ Hall B
Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search
Youhei Akimoto · Shinichi Shirakawa · Nozomu Yoshinari · Kento Uchida · Shota Saito · Kouhei Nishida
Oral
Tue Jun 11th 04:30 -- 04:35 PM @ Grand Ballroom
Scalable Fair Clustering
Arturs Backurs · Piotr Indyk · Krzysztof Onak · Baruch Schieber · Ali Vakilian · Tal Wagner
Oral
Tue Jun 11th 04:30 -- 04:35 PM @ Room 102
An Optimal Private Stochastic-MAB Algorithm based on Optimal Private Stopping Rule
Touqir Sajed · Or Sheffet
Oral
Tue Jun 11th 04:30 -- 04:35 PM @ Hall A
Shallow-Deep Networks: Understanding and Mitigating Network Overthinking
Yigitcan Kaya · Sanghyun Hong · Tudor Dumitras
Oral
Tue Jun 11th 04:30 -- 04:35 PM @ Hall B
A Quantitative Analysis of the Effect of Batch Normalization on Gradient Descent
YongQiang Cai · Qianxiao Li · Zuowei Shen
Oral
Tue Jun 11th 04:30 -- 04:35 PM @ Room 104
Per-Decision Option Discounting
Anna Harutyunyan · Peter Vrancx · Philippe Hamel · Ann Nowe · Doina Precup
Oral
Tue Jun 11th 04:30 -- 04:35 PM @ Seaside Ballroom
GDPP: Learning Diverse Generations using Determinantal Point Processes
Mohamed Elfeki · Camille Couprie · Morgane Riviere · Mohamed Elhoseiny
Oral
Tue Jun 11th 04:30 -- 04:35 PM @ Room 101
Neurally-Guided Structure Inference
Sidi Lu · Jiayuan Mao · Josh Tenenbaum · Jiajun Wu
Oral
Tue Jun 11th 04:30 -- 04:35 PM @ Room 103
Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints
Andrew Cotter · Maya Gupta · Heinrich Jiang · Nati Srebro · Karthik Sridharan · Serena Wang · Blake Woodworth · Seungil You
Oral
Tue Jun 11th 04:30 -- 04:35 PM @ Room 201
Optimal Minimal Margin Maximization with Boosting
Alexander Mathiasen · Kasper Green Larsen · Allan Grønlund
Oral
Tue Jun 11th 04:35 -- 04:40 PM @ Seaside Ballroom
Co-Representation Network for Generalized Zero-Shot Learning
Fei Zhang · Guangming Shi
Oral
Tue Jun 11th 04:35 -- 04:40 PM @ Room 103
Optimality Implies Kernel Sum Classifiers are Statistically Efficient
Raphael Meyer · Jean Honorio
Oral
Tue Jun 11th 04:35 -- 04:40 PM @ Grand Ballroom
Conditional Gradient Methods via Stochastic Path-Integrated Differential Estimator
Alp Yurtsever · Suvrit Sra · Volkan Cevher
Oral
Tue Jun 11th 04:35 -- 04:40 PM @ Room 104
Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds
Andrea Zanette · Emma Brunskill
Oral
Tue Jun 11th 04:35 -- 04:40 PM @ Hall A
Graph U-Nets
Hongyang Gao · Shuiwang Ji
Oral
Tue Jun 11th 04:35 -- 04:40 PM @ Hall B
The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study
Daniel Park · Jascha Sohl-Dickstein · Quoc Le · Samuel L Smith
Oral
Tue Jun 11th 04:35 -- 04:40 PM @ Room 101
Bayesian Joint Spike-and-Slab Graphical Lasso
Zehang Li · Tyler Mccormick · Samuel Clark
Oral
Tue Jun 11th 04:35 -- 04:40 PM @ Room 201
Generalized Linear Rule Models
Dennis Wei · Sanjeeb Dash · Tian Gao · Oktay Gunluk
Oral
Tue Jun 11th 04:35 -- 04:40 PM @ Room 102
Sublinear Space Private Algorithms Under the Sliding Window Model
Jalaj Upadhyay
Oral
Tue Jun 11th 04:40 -- 05:00 PM @ Room 102
Locally Private Bayesian Inference for Count Models
Aaron Schein · Steven Wu · Alexandra Schofield · Mingyuan Zhou · Hanna Wallach
Oral
Tue Jun 11th 04:40 -- 05:00 PM @ Seaside Ballroom
GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects
Edward Smith · Edward Smith · Scott Fujimoto · Adriana Romero · Scott Fujimoto · Adriana Romero · David Meger · David Meger
Oral
Tue Jun 11th 04:40 -- 05:00 PM @ Room 101
Rotation Invariant Householder Parameterization for Bayesian PCA
Rajbir-Singh Nirwan · Nils Bertschinger
Oral
Tue Jun 11th 04:40 -- 05:00 PM @ Room 103
The Implicit Fairness Criterion of Unconstrained Learning
Lydia T. Liu · Max Simchowitz · University of California Moritz Hardt
Oral
Tue Jun 11th 04:40 -- 05:00 PM @ Hall B
AdaGrad stepsizes: sharp convergence over nonconvex landscapes
Rachel Ward · Xiaoxia Wu · Leon Bottou
Oral
Tue Jun 11th 04:40 -- 05:00 PM @ Room 201
Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and Applications
Pin-Yu Chen · Lingfei Wu · Sijia Liu · Indika Rajapakse
Oral
Tue Jun 11th 04:40 -- 05:00 PM @ Hall A
SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver
Po-Wei Wang · Priya Donti · Bryan Wilder · Zico Kolter
Oral
Tue Jun 11th 04:40 -- 05:00 PM @ Grand Ballroom
Fault Tolerance in Iterative-Convergent Machine Learning
Aurick Qiao · Bryon Aragam · Bingjing Zhang · Eric Xing
Oral
Tue Jun 11th 04:40 -- 05:00 PM @ Room 104
A Theory of Regularized Markov Decision Processes
Matthieu Geist · Bruno Scherrer · Olivier Pietquin
Oral
Tue Jun 11th 05:00 -- 05:05 PM @ Room 103
Weak Detection of Signal in the Spiked Wigner Model
Hye Won Chung · Ji Oon Lee
Oral
Tue Jun 11th 05:00 -- 05:05 PM @ Room 101
A Framework for Bayesian Optimization in Embedded Subspaces
Amin Nayebi · Alexander Munteanu · Matthias Poloczek
Oral
Tue Jun 11th 05:00 -- 05:05 PM @ Hall A
Area Attention
Yang Li · Lukasz Kaiser · Samy Bengio · Si Si
Oral
Tue Jun 11th 05:00 -- 05:05 PM @ Room 201
Variational Inference for sparse network reconstruction from count data
Julien Chiquet · Stephane Robin · Mahendra Mariadassou
Oral
Tue Jun 11th 05:00 -- 05:05 PM @ Grand Ballroom
Static Automatic Batching In TensorFlow
Ashish Agarwal
Oral
Tue Jun 11th 05:00 -- 05:05 PM @ Hall B
Beyond Backprop: Online Alternating Minimization with Auxiliary Variables
Anna Choromanska · Benjamin Cowen · Sadhana Kumaravel · Ronny Luss · Mattia Rigotti · Irina Rish · Paolo DiAchille · Viatcheslav Gurev · Brian Kingsbury · Ravi Tejwani · Djallel Bouneffouf
Oral
Tue Jun 11th 05:00 -- 05:05 PM @ Seaside Ballroom
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Mingxing Tan · Quoc Le
Oral
Tue Jun 11th 05:00 -- 05:05 PM @ Room 102
Low Latency Privacy Preserving Inference
Alon Brutzkus · Ran Gilad-Bachrach · Oren Elisha
Oral
Tue Jun 11th 05:00 -- 05:05 PM @ Room 104
Discovering Options for Exploration by Minimizing Cover Time
Yuu Jinnai · Jee Won Park · David Abel · George Konidaris
Oral
Tue Jun 11th 05:05 -- 05:10 PM @ Room 102
Communication Complexity in Locally Private Distribution Estimation and Heavy Hitters
Jayadev Acharya · Ziteng Sun
Oral
Tue Jun 11th 05:05 -- 05:10 PM @ Hall B
SWALP : Stochastic Weight Averaging in Low Precision Training
Guandao Yang · Tianyi Zhang · Polina Kirichenko · Junwen Bai · Andrew Wilson · Christopher De Sa
Oral
Tue Jun 11th 05:05 -- 05:10 PM @ Grand Ballroom
Improving Neural Network Quantization without Retraining using Outlier Channel Splitting
Ritchie Zhao · Yuwei Hu · Jordan Dotzel · Christopher De Sa · Zhiru Zhang
Oral
Tue Jun 11th 05:05 -- 05:10 PM @ Room 104
Policy Certificates: Towards Accountable Reinforcement Learning
Christoph Dann · Lihong Li · Wei Wei · Emma Brunskill
Oral
Tue Jun 11th 05:05 -- 05:10 PM @ Room 201
Simplifying Graph Convolutional Networks
Felix Wu · Amauri Souza · Tianyi Zhang · Christopher Fifty · Tao Yu · Kilian Weinberger
Oral
Tue Jun 11th 05:05 -- 05:10 PM @ Seaside Ballroom
Geometry Aware Convolutional Filters for Omnidirectional Images Representation
Renata Khasanova · Pascal Frossard
Oral
Tue Jun 11th 05:05 -- 05:10 PM @ Room 101
Convolutional Poisson Gamma Belief Network
CHAOJIE WANG · Bo Chen · SUCHENG XIAO · Mingyuan Zhou
Oral
Tue Jun 11th 05:05 -- 05:10 PM @ Hall A
The Evolved Transformer
David So · Quoc Le · Chen Liang
Oral
Tue Jun 11th 05:05 -- 05:10 PM @ Room 103
Rademacher Complexity for Adversarially Robust Generalization
Dong Yin · Kannan Ramchandran · Peter Bartlett
Oral
Tue Jun 11th 05:10 -- 05:15 PM @ Seaside Ballroom
A Personalized Affective Memory Model for Improving Emotion Recognition
Pablo Barros · German Parisi · Stefan Wermter
Oral
Tue Jun 11th 05:10 -- 05:15 PM @ Hall B
Efficient optimization of loops and limits with randomized telescoping sums
Alex Beatson · Ryan P Adams
Oral
Tue Jun 11th 05:10 -- 05:15 PM @ Room 103
Provably efficient RL with Rich Observations via Latent State Decoding
Simon Du · Akshay Krishnamurthy · Nan Jiang · Alekh Agarwal · Miroslav Dudik · John Langford
Oral
Tue Jun 11th 05:10 -- 05:15 PM @ Room 101
Automatic Posterior Transformation for Likelihood-Free Inference
David Greenberg · Marcel Nonnenmacher · Jakob Macke
Oral
Tue Jun 11th 05:10 -- 05:15 PM @ Room 201
Robust Influence Maximization for Hyperparametric Models
Dimitrios Kalimeris · Gal Kaplun · Yaron Singer
Oral
Tue Jun 11th 05:10 -- 05:15 PM @ Room 102
Poission Subsampled R\'enyi Differential Privacy
Yuqing Zhu · Yu-Xiang Wang
Oral
Tue Jun 11th 05:10 -- 05:15 PM @ Room 104
Action Robust Reinforcement Learning and Applications in Continuous Control
Chen Tessler · Chen Tessler · Yonathan Efroni · Yonathan Efroni · Shie Mannor · Shie Mannor
Oral
Tue Jun 11th 05:10 -- 05:15 PM @ Grand Ballroom
Memory-Optimal Direct Convolutions for Maximizing Classification Accuracy in Embedded Applications
Albert Gural · Boris Murmann
Oral
Tue Jun 11th 05:10 -- 05:15 PM @ Hall A
Jumpout : Improved Dropout for Deep Neural Networks with ReLUs
Shengjie Wang · Tianyi Zhou · Jeff Bilmes
Oral
Tue Jun 11th 05:15 -- 05:20 PM @ Room 101
Active Learning for Decision-Making from Imbalanced Observational Data
Iiris Sundin · Peter Schulam · Eero Siivola · Aki Vehtari · Suchi Saria · Samuel Kaski
Oral
Tue Jun 11th 05:15 -- 05:20 PM @ Room 102
Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCA
Jordan Awan · Ana Kenney · Matthew Reimherr · Aleksandra Slavković
Oral
Tue Jun 11th 05:15 -- 05:20 PM @ Grand Ballroom
DL2: Training and Querying Neural Networks with Logic
Marc Fischer · Mislav Balunovic · Dana Drachsler-Cohen · Timon Gehr · Ce Zhang · Martin Vechev
Oral
Tue Jun 11th 05:15 -- 05:20 PM @ Room 104
The Value Function Polytope in Reinforcement Learning
Robert Dadashi · Marc Bellemare · Adrien Ali Taiga · Nicolas Le Roux · Dale Schuurmans
Oral
Tue Jun 11th 05:15 -- 05:20 PM @ Hall A
Stochastic Deep Networks
Gwendoline De Bie · Gabriel Peyré · Marco Cuturi
Oral
Tue Jun 11th 05:15 -- 05:20 PM @ Room 103
Information-Theoretic Considerations in Batch Reinforcement Learning
Jinglin Chen · Nan Jiang
Oral
Tue Jun 11th 05:15 -- 05:20 PM @ Seaside Ballroom
Temporal Gaussian Mixture Layer for Videos
AJ Piergiovanni · Michael Ryoo
Oral
Tue Jun 11th 05:15 -- 05:20 PM @ Hall B
Self-similar Epochs: Value in arrangement
Eliav Buchnik · Edith Cohen · Avinatan Hasidim · Yossi Matias
Oral
Tue Jun 11th 05:15 -- 05:20 PM @ Room 201
HyperGAN: A Generative Model for Diverse, Performant Neural Networks
Neale Ratzlaff · Fuxin Li
Break
Tue Jun 11th 05:30 -- 06:00 PM @ None
Light Evening Snack
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #1
SelectiveNet: A Deep Neural Network with an Integrated Reject Option
Yonatan Geifman · Ran El-Yaniv
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #2
Manifold Mixup: Better Representations by Interpolating Hidden States
Vikas Verma · Alex Lamb · Christopher Beckham · Amir Najafi · Ioannis Mitliagkas · David Lopez-Paz · Yoshua Bengio
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #3
Processing Megapixel Images with Deep Attention-Sampling Models
Angelos Katharopoulos · Francois Fleuret
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #4
TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning
Sung Whan Yoon · Jun Seo · Jaekyun Moon
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #5
Online Meta-Learning
Chelsea Finn · Aravind Rajeswaran · Sham Kakade · Sergey Levine
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #6
Training Neural Networks with Local Error Signals
Arild Nøkland · Lars Hiller Eidnes
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #7
GMNN: Graph Markov Neural Networks
Meng Qu · Yoshua Bengio · Jian Tang
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #8
Self-Attention Graph Pooling
Junhyun Lee · Inyeop Lee · Jaewoo Kang
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #9
Combating Label Noise in Deep Learning using Abstention
Sunil Thulasidasan · Tanmoy Bhattacharya · Jeff Bilmes · Gopinath Chennupati · Jamal Mohd-Yusof
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #10
LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning
Huaiyu Li · Weiming Dong · Xing Mei · Chongyang Ma · Feiyue Huang · Bao-Gang Hu
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #11
Self-Attention Generative Adversarial Networks
Han Zhang · Ian Goodfellow · Dimitris Metaxas · Augustus Odena
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #12
Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution Matching
Ziliang Chen · ZHANFU YANG · Xiaoxi Wang · Xiaodan Liang · xiaopeng yan · Guanbin Li · Liang Lin
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #13
High-Fidelity Image Generation With Fewer Labels
Mario Lucic · Michael Tschannen · Marvin Ritter · Xiaohua Zhai · Olivier Bachem · Sylvain Gelly
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #14
Revisiting precision recall definition for generative modeling
Loic Simon · Ryan Webster · Julien Rabin
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #15
Wasserstein of Wasserstein Loss for Learning Generative Models
Yonatan Dukler · Wuchen Li · Alex Lin · Guido Montufar
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #16
Flat Metric Minimization with Applications in Generative Modeling
Thomas Möllenhoff · Daniel Cremers
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #17
Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs
Yogesh Balaji · Hamed Hassani · Rama Chellappa · Soheil Feizi
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #18
Non-Parametric Priors For Generative Adversarial Networks
Rajhans Singh · Pavan Turaga · Suren Jayasuriya · Ravi Garg · Martin Braun
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #19
Lipschitz Generative Adversarial Nets
Zhiming Zhou · Jiadong Liang · Yuxuan Song · Lantao Yu · Hongwei Wang · Weinan Zhang · Yong Yu · Zhihua Zhang
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #20
HexaGAN: Generative Adversarial Nets for Real World Classification
Uiwon Hwang · Dahuin Jung · Sungroh Yoon
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #21
Graph Matching Networks for Learning the Similarity of Graph Structured Objects
Yujia Li · Chenjie Gu · Thomas Dullien · Oriol Vinyals · Pushmeet Kohli
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #22
BayesNAS: A Bayesian Approach for Neural Architecture Search
Hongpeng Zhou · Minghao Yang · Jun Wang · Wei Pan
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #23
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks
Juho Lee · Yoonho Lee · Jungtaek Kim · Adam Kosiorek · Seungjin Choi · Yee Whye Teh
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #24
Shallow-Deep Networks: Understanding and Mitigating Network Overthinking
Yigitcan Kaya · Sanghyun Hong · Tudor Dumitras
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #25
Graph U-Nets
Hongyang Gao · Shuiwang Ji
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #26
SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver
Po-Wei Wang · Priya Donti · Bryan Wilder · Zico Kolter
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #27
Area Attention
Yang Li · Lukasz Kaiser · Samy Bengio · Si Si
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #28
The Evolved Transformer
David So · Quoc Le · Chen Liang
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #29
Jumpout : Improved Dropout for Deep Neural Networks with ReLUs
Shengjie Wang · Tianyi Zhou · Jeff Bilmes
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #30
Stochastic Deep Networks
Gwendoline De Bie · Gabriel Peyré · Marco Cuturi
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #31
ELF OpenGo: an analysis and open reimplementation of AlphaZero
Yuandong Tian · Jerry Ma · Qucheng Gong · Shubho Sengupta · Zhuoyuan Chen · James Pinkerton · Larry Zitnick
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #32
Making Deep Q-learning methods robust to time discretization
Corentin Tallec · Leonard Blier · Yann Ollivier
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #33
Nonlinear Distributional Gradient Temporal-Difference Learning
chao qu · Shie Mannor · Huan Xu
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #34
Composing Entropic Policies using Divergence Correction
Jonathan Hunt · Andre Barreto · Timothy Lillicrap · Nicolas Heess
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #35
TibGM: A Transferable and Information-Based Graphical Model Approach for Reinforcement Learning
Tameem Adel · Adrian Weller
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #36
Multi-Agent Adversarial Inverse Reinforcement Learning
Lantao Yu · Jiaming Song · Stefano Ermon
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #37
Policy Consolidation for Continual Reinforcement Learning
Christos Kaplanis · Murray Shanahan · Claudia Clopath
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #38
Off-Policy Deep Reinforcement Learning without Exploration
Scott Fujimoto · David Meger · Doina Precup
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #39
Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation
Ruohan Wang · Carlo Ciliberto · Pierluigi Vito Amadori · Yiannis Demiris
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #40
Revisiting the Softmax Bellman Operator: New Benefits and New Perspective
Zhao Song · Ron Parr · Lawrence Carin
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #41
An Investigation of Model-Free Planning
Arthur Guez · Mehdi Mirza · Karol Gregor · Rishabh Kabra · Sebastien Racaniere · Theophane Weber · David Raposo · Adam Santoro · Laurent Orseau · Tom Eccles · Greg Wayne · David Silver · Timothy Lillicrap
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #42
CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning
Cédric Colas · Pierre-Yves Oudeyer · Olivier Sigaud · Pierre Fournier · Mohamed Chetouani
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #43
Task-Agnostic Dynamics Priors for Deep Reinforcement Learning
Yilun Du · Karthik Narasimhan
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #44
Diagnosing Bottlenecks in Deep Q-learning Algorithms
Justin Fu · Aviral Kumar · Matthew Soh · Sergey Levine
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #45
Collaborative Evolutionary Reinforcement Learning
Shauharda Khadka · Somdeb Majumdar · Tarek Nassar · Zach Dwiel · Evren Tumer · Santiago Miret · Yinyin Liu · Kagan Tumer
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #46
EMI: Exploration with Mutual Information
Hyoungseok Kim · Jaekyeom Kim · Yeonwoo Jeong · Sergey Levine · Hyun Oh Song
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #47
Imitation Learning from Imperfect Demonstration
Yueh-Hua Wu · Nontawat Charoenphakdee · Han Bao · Voot Tangkaratt · Masashi Sugiyama
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #48
Curiosity-Bottleneck: Exploration By Distilling Task-Specific Novelty
Youngjin Kim · Daniel Nam · Hyunwoo Kim · Ji-Hoon Kim · Gunhee Kim
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #49
Dynamic Weights in Multi-Objective Deep Reinforcement Learning
Axel Abels · Diederik Roijers · Tom Lenaerts · Ann Nowé · Denis Steckelmacher
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #50
Fingerprint Policy Optimisation for Robust Reinforcement Learning
Supratik Paul · Michael A Osborne · Shimon Whiteson
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #51
An Investigation into Neural Net Optimization via Hessian Eigenvalue Density
Behrooz Ghorbani · Shankar Krishnan · Ying Xiao
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #52
Differentiable Linearized ADMM
Xingyu Xie · Jianlong Wu · Guangcan Liu · Zhisheng Zhong · Zhouchen Lin
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #53
Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search
Youhei Akimoto · Shinichi Shirakawa · Nozomu Yoshinari · Kento Uchida · Shota Saito · Kouhei Nishida
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #54
A Quantitative Analysis of the Effect of Batch Normalization on Gradient Descent
YongQiang Cai · Qianxiao Li · Zuowei Shen
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #55
The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study
Daniel Park · Jascha Sohl-Dickstein · Quoc Le · Samuel L Smith
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #56
AdaGrad stepsizes: sharp convergence over nonconvex landscapes
Rachel Ward · Xiaoxia Wu · Leon Bottou
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #57
Beyond Backprop: Online Alternating Minimization with Auxiliary Variables
Anna Choromanska · Benjamin Cowen · Sadhana Kumaravel · Ronny Luss · Mattia Rigotti · Irina Rish · Paolo DiAchille · Viatcheslav Gurev · Brian Kingsbury · Ravi Tejwani · Djallel Bouneffouf
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #58
SWALP : Stochastic Weight Averaging in Low Precision Training
Guandao Yang · Tianyi Zhang · Polina Kirichenko · Junwen Bai · Andrew Wilson · Christopher De Sa
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #59
Efficient optimization of loops and limits with randomized telescoping sums
Alex Beatson · Ryan P Adams
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #60
Self-similar Epochs: Value in arrangement
Eliav Buchnik · Edith Cohen · Avinatan Hasidim · Yossi Matias
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #61
Adversarial Attacks on Node Embeddings via Graph Poisoning
Aleksandar Bojchevski · Stephan Günnemann
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #62
First-Order Adversarial Vulnerability of Neural Networks and Input Dimension
Carl-Johann Simon-Gabriel · Yann Ollivier · Leon Bottou · Bernhard Schölkopf · David Lopez-Paz
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #63
On Certifying Non-Uniform Bounds against Adversarial Attacks
Chen Liu · Ryota Tomioka · Volkan Cevher
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #64
Improving Adversarial Robustness via Promoting Ensemble Diversity
Tianyu Pang · Kun Xu · Chao Du · Ning Chen · Jun Zhu
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #65
Adversarial camera stickers: A physical camera-based attack on deep learning systems
Juncheng Li · Frank Schmidt · Zico Kolter
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #66
Adversarial examples from computational constraints
Sebastien Bubeck · Yin Tat Lee · Eric Price · Ilya Razenshteyn
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #67
POPQORN: Quantifying Robustness of Recurrent Neural Networks
CHING-YUN KO · Zhaoyang Lyu · Tsui-Wei Weng · Luca Daniel · Ngai Wong · Dahua Lin
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #68
Using Pre-Training Can Improve Model Robustness and Uncertainty
Dan Hendrycks · Kimin Lee · Mantas Mazeika
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #69
Generalized No Free Lunch Theorem for Adversarial Robustness
Elvis Dohmatob
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #70
PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach
Tsui-Wei Weng · Pin-Yu Chen · Lam Nguyen · Mark Squillante · Akhilan Boopathy · Ivan Oseledets · Luca Daniel
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #71
On Learning Invariant Representations for Domain Adaptation
Han Zhao · Remi Tachet des Combes · Kun Zhang · Geoff Gordon
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #72
Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models
Mor Shpigel Nacson · Suriya Gunasekar · Jason Lee · Nati Srebro · Daniel Soudry
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #73
Adversarial Generation of Time-Frequency Features with application in audio synthesis
Andrés Marafioti · Nathanaël Perraudin · Nicki Holighaus · Piotr Majdak
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #74
On the Universality of Invariant Networks
Haggai Maron · Ethan Fetaya · Nimrod Segol · Yaron Lipman
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #75
Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks
Sanjeev Arora · Simon Du · Wei Hu · Zhiyuan Li · Ruosong Wang
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #76
Gauge Equivariant Convolutional Networks and the Icosahedral CNN
Taco Cohen · Maurice Weiler · Berkay Kicanaoglu · Max Welling
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #77
Feature-Critic Networks for Heterogeneous Domain Generalization
Yiying Li · Yongxin Yang · Wei Zhou · Timothy Hospedales
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #78
Learning to Convolve: A Generalized Weight-Tying Approach
Nichita Diaconu · Daniel E Worrall
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #79
On Dropout and Nuclear Norm Regularization
Poorya Mianjy · Raman Arora
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #80
Gradient Descent Finds Global Minima of Deep Neural Networks
Simon Du · Jason Lee · Haochuan Li · Liwei Wang · Xiyu Zhai
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #81
Composable Core-sets for Determinant Maximization: A Simple Near-Optimal Algorithm
Sepideh Mahabadi · Piotr Indyk · Shayan Oveis Gharan · Alireza Rezaei
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #82
Sublinear Time Nearest Neighbor Search over Generalized Weighted Space
Yifan Lei · Qiang Huang · Mohan Kankanhalli · Anthony Tung
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #83
Compressing Gradient Optimizers via Count-Sketches
Ryan Spring · Anastasios Kyrillidis · Vijai Mohan · Anshumali Shrivastava
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #84
Scalable Fair Clustering
Arturs Backurs · Piotr Indyk · Krzysztof Onak · Baruch Schieber · Ali Vakilian · Tal Wagner
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #85
Conditional Gradient Methods via Stochastic Path-Integrated Differential Estimator
Alp Yurtsever · Suvrit Sra · Volkan Cevher
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #86
Fault Tolerance in Iterative-Convergent Machine Learning
Aurick Qiao · Bryon Aragam · Bingjing Zhang · Eric Xing
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #87
Static Automatic Batching In TensorFlow
Ashish Agarwal
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #88
Improving Neural Network Quantization without Retraining using Outlier Channel Splitting
Ritchie Zhao · Yuwei Hu · Jordan Dotzel · Christopher De Sa · Zhiru Zhang
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #89
Memory-Optimal Direct Convolutions for Maximizing Classification Accuracy in Embedded Applications
Albert Gural · Boris Murmann
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #90
DL2: Training and Querying Neural Networks with Logic
Marc Fischer · Mislav Balunovic · Dana Drachsler-Cohen · Timon Gehr · Ce Zhang · Martin Vechev
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #91
PA-GD: On the Convergence of Perturbed Alternating Gradient Descent to Second-Order Stationary Points for Structured Nonconvex Optimization
Songtao Lu · Mingyi Hong · Zhengdao Wang
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #92
Improved Zeroth-Order Variance Reduced Algorithms and Analysis for Nonconvex Optimization
Kaiyi Ji · Zhe Wang · Yi Zhou · Yingbin LIANG
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #93
Faster Stochastic Alternating Direction Method of Multipliers for Nonconvex Optimization
Feihu Huang · Songcan Chen · Heng Huang
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #94
Lower Bounds for Smooth Nonconvex Finite-Sum Optimization
Dongruo Zhou · Quanquan Gu
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #95
Nonconvex Variance Reduced Optimization with Arbitrary Sampling
Samuel Horvath · Peter Richtarik
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #96
Error Feedback Fixes SignSGD and other Gradient Compression Schemes
Sai Praneeth Reddy Karimireddy · Quentin Rebjock · Sebastian Stich · Martin Jaggi
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #97
A Composite Randomized Incremental Gradient Method
Junyu Zhang · Lin Xiao
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #98
Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field Inference
Yatao (An) Bian · Joachim Buhmann · Andreas Krause
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #99
Multiplicative Weights Updates as a distributed constrained optimization algorithm: Convergence to second-order stationary points almost always
Ioannis Panageas · Georgios Piliouras · xiao wang
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #100
Katalyst: Boosting Convex Katayusha for Non-Convex Problems with a Large Condition Number
Zaiyi Chen · Yi Xu · Haoyuan Hu · Tianbao Yang
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #101
Safe Policy Improvement with Baseline Bootstrapping
Romain Laroche · Paul TRICHELAIR · Remi Tachet des Combes
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #102
Distributional Reinforcement Learning for Efficient Exploration
Borislav Mavrin · Hengshuai Yao · Linglong Kong · Kaiwen Wu · Yaoliang Yu
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #103
Optimistic Policy Optimization via Multiple Importance Sampling
Matteo Papini · Alberto Maria Metelli · Lorenzo Lupo · Marcello Restelli
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #104
Neural Logic Reinforcement Learning
zhengyao jiang · Shan Luo
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #105
Learning to Collaborate in Markov Decision Processes
Goran Radanovic · Rati Devidze · David Parkes · Adish Singla
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #106
Predictor-Corrector Policy Optimization
Ching-An Cheng · Xinyan Yan · Nathan Ratliff · Byron Boots
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #107
Learning a Prior over Intent via Meta-Inverse Reinforcement Learning
Kelvin Xu · Ellis Ratner · EECS Anca Dragan · Sergey Levine · Chelsea Finn
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #108
DeepMDP: Learning Continuous Latent Space Models for Representation Learning
Carles Gelada · Saurabh Kumar · Jacob Buckman · Ofir Nachum · Marc Bellemare
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #109
Importance Sampling Policy Evaluation with an Estimated Behavior Policy
Josiah Hanna · Scott Niekum · Peter Stone
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #110
Learning from a Learner
alexis jacq · Matthieu Geist · Ana Paiva · Olivier Pietquin
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #111
Separable value functions across time-scales
Joshua Romoff · Peter Henderson · Ahmed Touati · Yann Ollivier · Joelle Pineau · Emma Brunskill
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #112
Learning Action Representations for Reinforcement Learning
Yash Chandak · Georgios Theocharous · James Kostas · Scott Jordan · Philip Thomas
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #113
Bayesian Counterfactual Risk Minimization
Ben London · Ted Sandler
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #114
Per-Decision Option Discounting
Anna Harutyunyan · Peter Vrancx · Philippe Hamel · Ann Nowe · Doina Precup
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #115
Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds
Andrea Zanette · Emma Brunskill
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #116
A Theory of Regularized Markov Decision Processes
Matthieu Geist · Bruno Scherrer · Olivier Pietquin
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #117
Discovering Options for Exploration by Minimizing Cover Time
Yuu Jinnai · Jee Won Park · David Abel · George Konidaris
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #118
Policy Certificates: Towards Accountable Reinforcement Learning
Christoph Dann · Lihong Li · Wei Wei · Emma Brunskill
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #119
The Value Function Polytope in Reinforcement Learning
Robert Dadashi · Marc Bellemare · Adrien Ali Taiga · Nicolas Le Roux · Dale Schuurmans
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #120
Data Shapley: Equitable Valuation of Data for Machine Learning
Amirata Ghorbani · James Zou
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #121
Feature Grouping as a Stochastic Regularizer for High-Dimensional Structured Data
Sergul Aydore · Thirion Bertrand · Gael Varoquaux
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #122
Metric-Optimized Example Weights
Sen Zhao · Mahdi Milani Fard · Harikrishna Narasimhan · Maya Gupta
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #123
Improving Model Selection by Employing the Test Data
Max Westphal · Werner Brannath
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #124
Topological Data Analysis of Decision Boundaries with Application to Model Selection
Karthikeyan Ramamurthy · Kush Varshney · Krishnan Mody
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #125
Contextual Memory Trees
Wen Sun · Alina Beygelzimer · Hal Daume · John Langford · Paul Mineiro
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #126
Sparse Extreme Multi-label Learning with Oracle Property
Weiwei Liu · Xiaobo Shen
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #127
Shape Constraints for Set Functions
Andrew Cotter · Maya Gupta · Heinrich Jiang · Erez Louidor · James Muller · Tamann Narayan · Serena Wang · Tao Zhu
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #128
On The Power of Curriculum Learning in Training Deep Networks
Guy Hacohen · Daphna Weinshall
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #129
Voronoi Boundary Classification: A High-Dimensional Geometric Approach via Weighted Monte Carlo Integration
Vladislav Polianskii · Florian Pokorny
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #130
Robust Decision Trees Against Adversarial Examples
Hongge Chen · Huan Zhang · Duane Boning · Cho-Jui Hsieh
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #131
Automatic Classifiers as Scientific Instruments: One Step Further Away from Ground-Truth
Jacob Whitehill · Anand Ramakrishnan
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #132
Look Ma, No Latent Variables: Accurate Cutset Networks via Compilation
Tahrima Rahman · Shasha Jin · Vibhav Gogate
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #133
Optimal Transport for structured data with application on graphs
Titouan Vayer · Nicolas Courty · Romain Tavenard · Chapel Laetitia · Remi Flamary
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #134
Learning Optimal Linear Regularizers
Matthew Streeter
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #135
On Symmetric Losses for Learning from Corrupted Labels
Nontawat Charoenphakdee · Jongyeong Lee · Masashi Sugiyama
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #136
AUCµ: A Performance Metric for Multi-Class Machine Learning Models
Ross Kleiman · University of Wisconsin David Page
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #137
Regularization in directable environments with application to Tetris
Jan Malte Lichtenberg · Ozgur Simsek
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #138
Improved Dynamic Graph Learning through Fault-Tolerant Sparsification
Chunjiang Zhu · Sabine Storandt · Kam-Yiu Lam · Song Han · Jinbo Bi
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #139
Heterogeneous Model Reuse via Optimizing Multiparty Multiclass Margin
Xi-Zhu Wu · Song Liu · Zhi-Hua Zhou
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #140
Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff
Yochai Blau · Tomer Michaeli
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #141
Collaborative Channel Pruning for Deep Networks
Hanyu Peng · Jiaxiang Wu · Shifeng Chen · Junzhou Huang
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #142
Same, Same But Different: Recovering Neural Network Quantization Error Through Weight Factorization
Eldad Meller · Alexander Finkelstein · Uri Almog · Mark Grobman
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #143
GDPP: Learning Diverse Generations using Determinantal Point Processes
Mohamed Elfeki · Camille Couprie · Morgane Riviere · Mohamed Elhoseiny
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #144
Co-Representation Network for Generalized Zero-Shot Learning
Fei Zhang · Guangming Shi
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #145
GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects
Edward Smith · Scott Fujimoto · Adriana Romero · David Meger
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #146
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Mingxing Tan · Quoc Le
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #147
Geometry Aware Convolutional Filters for Omnidirectional Images Representation
Renata Khasanova · Pascal Frossard
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #148
A Personalized Affective Memory Model for Improving Emotion Recognition
Pablo Barros · German Parisi · Stefan Wermter
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #149
Temporal Gaussian Mixture Layer for Videos
AJ Piergiovanni · Michael Ryoo
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #150
Regret Circuits: Composability of Regret Minimizers
Gabriele Farina · Christian Kroer · Tuomas Sandholm
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #151
Game Theoretic Optimization via Gradient-based Nikaido-Isoda Function
Arvind Raghunathan · Anoop Cherian · Devesh Jha
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #152
Stable-Predictive Optimistic Counterfactual Regret Minimization
Gabriele Farina · Christian Kroer · Noam Brown · Tuomas Sandholm
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #153
When Samples Are Strategically Selected
Hanrui Zhang · Yu Cheng · Vincent Conitzer
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #154
Statistical Foundations of Virtual Democracy
Anson Kahng · Min Kyung Lee · Ritesh Noothigattu · Ariel Procaccia · Christos-Alexandros Psomas
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #155
Optimal Auctions through Deep Learning
Paul Duetting · Zhe Feng · Harikrishna Narasimhan · David Parkes · Sai Srivatsa Ravindranath
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #156
Learning to Clear the Market
Weiran Shen · Sébastien Lahaie · Renato Leme
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #157
Learning to bid in revenue-maximizing auctions
Thomas Nedelec · Noureddine El Karoui · Vianney Perchet
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #158
Open-ended learning in symmetric zero-sum games
David Balduzzi · Marta Garnelo · Yoram Bachrach · Wojciech Czarnecki · Julien Perolat · Max Jaderberg · Thore Graepel
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #159
Deep Counterfactual Regret Minimization
Noam Brown · Adam Lerer · Sam Gross · Tuomas Sandholm
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #160
Generalized Approximate Survey Propagation for High-Dimensional Estimation
Carlo Lucibello · Luca Saglietti · Yue Lu
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #161
Boosted Density Estimation Remastered
Zac Cranko · Richard Nock
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #162
Inference and Sampling of $K_{33}$-free Ising Models
Valerii Likhosherstov · Yury Maximov · Misha Chertkov
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #163
Random Matrix Improved Covariance Estimation for a Large Class of Metrics
Malik TIOMOKO A · Romain Couillet · Florent BOUCHARD · Guillaume GINOLHAC
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #164
Dual Entangled Polynomial Code: Three-Dimensional Coding for Distributed Matrix Multiplication
Pedro Soto · Jun Li · Xiaodi Fan
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #165
Neural Joint Source-Channel Coding
Kristy Choi · Kedar Tatwawadi · Aditya Grover · Tsachy Weissman · Stefano Ermon
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #166
Doubly-Competitive Distribution Estimation
Yi Hao · Alon Orlitsky
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #167
Homomorphic Sensing
Manolis Tsakiris · Liangzu Peng
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #168
Phaseless PCA: Low-Rank Matrix Recovery from Column-wise Phaseless Measurements
Seyedehsara Nayer · Praneeth Narayanamurthy · Namrata Vaswani
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #169
Rate Distortion For Model Compression:From Theory To Practice
Weihao Gao · Yu-Han Liu · Chong Wang · Sewoong Oh
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #170
Formal Privacy for Functional Data with Gaussian Perturbations
Ardalan Mirshani · Matthew Reimherr · Aleksandra Slavković
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #171
Graphical-model based estimation and inference for differential privacy
Ryan McKenna · Daniel Sheldon · Gerome Miklau
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #172
White-box vs Black-box: Bayes Optimal Strategies for Membership Inference
Alexandre Sablayrolles · Douze Matthijs · Cordelia Schmid · Yann Ollivier · Herve Jegou
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #173
An Optimal Private Stochastic-MAB Algorithm based on Optimal Private Stopping Rule
Touqir Sajed · Or Sheffet
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #174
Sublinear Space Private Algorithms Under the Sliding Window Model
Jalaj Upadhyay
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #175
Locally Private Bayesian Inference for Count Models
Aaron Schein · Steven Wu · Alexandra Schofield · Mingyuan Zhou · Hanna Wallach
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #176
Low Latency Privacy Preserving Inference
Alon Brutzkus · Ran Gilad-Bachrach · Oren Elisha
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #177
Communication Complexity in Locally Private Distribution Estimation and Heavy Hitters
Jayadev Acharya · Ziteng Sun
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #178
Poission Subsampled R\'enyi Differential Privacy
Yuqing Zhu · Yu-Xiang Wang
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #179
Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCA
Jordan Awan · Ana Kenney · Matthew Reimherr · Aleksandra Slavković
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #180
Refined Complexity of PCA with Outliers
Kirill Simonov · Fedor Fomin · Petr Golovach · Fahad Panolan
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #181
On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms
Tianyi Lin · Nhat Ho · Michael Jordan
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #182
Passed & Spurious: Descent Algorithms and Local Minima in Spiked Matrix-Tensor Models
Stefano Sarao Mannelli · Florent Krzakala · Pierfrancesco Urbani · Lenka Zdeborova
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #183
Teaching a black-box learner
Sanjoy Dasgupta · Daniel Hsu · Stefanos Poulis · Jerry Zhu
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #184
PAC Learnability of Node Functions in Networked Dynamical Systems
Abhijin Adiga · Chris J Kuhlman · Madhav Marathe · S. S. Ravi · Anil Vullikanti
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #185
Online learning with kernel losses
Niladri S Chatterji · Aldo Pacchiano · Peter Bartlett
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #186
Nearest Neighbor and Kernel Survival Analysis: Nonasymptotic Error Bounds and Strong Consistency Rates
George Chen
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #187
Fast Rates for a kNN Classifier Robust to Unknown Asymmetric Label Noise
Henry Reeve · Ata Kaban
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #188
Uniform Convergence Rate of the Kernel Density Estimator Adaptive to Intrinsic Volume Dimension
Jisu Kim · Jaehyeok Shin · Alessandro Rinaldo · Larry Wasserman
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #189
Maximum Likelihood Estimation for Learning Populations of Parameters
Ramya Korlakai Vinayak · Weihao Kong · Gregory Valiant · Sham Kakade
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #190
Projection onto Minkowski Sums with Application to Constrained Learning
Joong-Ho Won · Jason Xu · Kenneth Lange
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #191
Blended Conditonal Gradients
Gábor Braun · Sebastian Pokutta · Dan Tu · Stephen Wright
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #192
Acceleration of SVRG and Katyusha X by Inexact Preconditioning
Yanli Liu · Fei Feng · Wotao Yin
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #193
Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD
Marten van Dijk · Lam Nguyen · PHUONG_HA NGUYEN · Dzung Phan
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #194
A Conditional-Gradient-Based Augmented Lagrangian Framework
Alp Yurtsever · Olivier Fercoq · Volkan Cevher
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #195
SGD: General Analysis and Improved Rates
Robert M. Gower · Nicolas Loizou · Xun Qian · Alibek Sailanbayev · Egor Shulgin · Peter Richtarik
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #196
Curvature-Exploiting Acceleration of Elastic Net Computations
Vien Van Mai · Mikael Johansson
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #197
Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication
Anastasiia Koloskova · Sebastian Stich · Martin Jaggi
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom
Safe Grid Search with Optimal Complexity
Eugene Ndiaye · Tam Le · Olivier Fercoq · Joseph Salmon · Ichiro Takeuchi
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #199
SAGA with Arbitrary Sampling
Xun Qian · Zheng Qu · Peter Richtarik
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #200
Natural Analysts in Adaptive Data Analysis
Tijana Zrnic · University of California Moritz Hardt
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #201
CapsAndRuns: An Improved Method for Approximately Optimal Algorithm Configuration
Gellért Weisz · Andras Gyorgy · Csaba Szepesvari
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #202
Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction
Giulia Luise · Dimitrios Stamos · Massimiliano Pontil · Carlo Ciliberto
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #203
Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints
Andrew Cotter · Maya Gupta · Heinrich Jiang · Nati Srebro · Karthik Sridharan · Serena Wang · Blake Woodworth · Seungil You
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #204
Optimality Implies Kernel Sum Classifiers are Statistically Efficient
Raphael Meyer · Jean Honorio
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #205
The Implicit Fairness Criterion of Unconstrained Learning
Lydia T. Liu · Max Simchowitz · University of California Moritz Hardt
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #206
Weak Detection of Signal in the Spiked Wigner Model
Hye Won Chung · Ji Oon Lee
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #207
Rademacher Complexity for Adversarially Robust Generalization
Dong Yin · Kannan Ramchandran · Peter Bartlett
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #208
Provably efficient RL with Rich Observations via Latent State Decoding
Simon Du · Akshay Krishnamurthy · Nan Jiang · Alekh Agarwal · Miroslav Dudik · John Langford
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #209
Information-Theoretic Considerations in Batch Reinforcement Learning
Jinglin Chen · Nan Jiang
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #210
A Contrastive Divergence for Combining Variational Inference and MCMC
Francisco Ruiz · Michalis Titsias
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #211
Calibrated Approximate Bayesian Inference
Hanwen Xing · Geoff Nicholls · Jeong Lee
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #212
Moment-Based Variational Inference for Markov Jump Processes
Christian Wildner · Heinz Koeppl
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #213
Understanding MCMC Dynamics as Flows on the Wasserstein Space
Chang Liu · Jingwei Zhuo · Jun Zhu
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #214
LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations
Brian Trippe · Jonathan Huggins · Raj Agrawal · Tamara Broderick
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #215
Amortized Monte Carlo Integration
Adam Golinski · Frank Wood · Tom Rainforth
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #216
Stein Point Markov Chain Monte Carlo
Wilson Ye Chen · Alessandro Barp · Francois-Xavier Briol · Jackson Gorham · Mark Girolami · Lester Mackey · Chris Oates
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #217
Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations
Wu Lin · Mohammad Emtiyaz Khan · Mark Schmidt
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #218
Particle Flow Bayes' Rule
Xinshi Chen · Hanjun Dai · Le Song
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #219
Correlated Variational Auto-Encoders
Da Tang · Dawen Liang · Tony Jebara · Nicholas Ruozzi
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #220
Towards a Unified Analysis of Random Fourier Features
Zhu Li · Jean-Francois Ton · Dino Oglic · Dino Sejdinovic
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #221
Learning deep kernels for exponential family densities
Li Kevin Wenliang · Dougal Sutherland · Heiko Strathmann · Arthur Gretton
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #222
Bayesian Deconditional Kernel Mean Embeddings
Kelvin Hsu · Fabio Ramos
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #223
A Kernel Perspective for Regularizing Deep Neural Networks
Alberto Bietti · Gregoire Mialon · Dexiong Chen · Julien Mairal
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #224
A Persistent Weisfeiler--Lehman Procedure for Graph Classification
Bastian Rieck · Christian Bock · Karsten Borgwardt
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #225
Rehashing Kernel Evaluation in High Dimensions
Paris Siminelakis · Kexin Rong · Peter Bailis · Moses Charikar · Philip Levis
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #226
Large-Scale Sparse Kernel Canonical Correlation Analysis
Viivi Uurtio · Sahely Bhadra · Juho Rousu
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #227
A Kernel Theory of Modern Data Augmentation
Tri Dao · Albert Gu · Alexander J Ratner · Virginia Smith · Christopher De Sa · Christopher Re
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #228
kernelPSI: a Post-Selection Inference Framework for Nonlinear Variable Selection
Lotfi Slim · Clément Chatelain · Chloe-Agathe Azencott · Jean-Philippe Vert
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #229
Scalable Learning in Reproducing Kernel Krein Spaces
Dino Oglic · Thomas Gaertner
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #230
Dirichlet Simplex Nest and Geometric Inference
Mikhail Yurochkin · Aritra Guha · Yuekai Sun · XuanLong Nguyen
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #231
Bayesian leave-one-out cross-validation for large data
Måns Magnusson · Michael Andersen · Johan Jonasson · Aki Vehtari
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #232
Rao-Blackwellized Stochastic Gradients for Discrete Distributions
Runjing Liu · Jeffrey Regier · Nilesh Tripuraneni · Michael Jordan · Jon McAuliffe
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #233
Neurally-Guided Structure Inference
Sidi Lu · Jiayuan Mao · Josh Tenenbaum · Jiajun Wu
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #234
Bayesian Joint Spike-and-Slab Graphical Lasso
Zehang Li · Tyler Mccormick · Samuel Clark
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #235
Rotation Invariant Householder Parameterization for Bayesian PCA
Rajbir-Singh Nirwan · Nils Bertschinger
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #236
A Framework for Bayesian Optimization in Embedded Subspaces
Amin Nayebi · Alexander Munteanu · Matthias Poloczek
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #237
Convolutional Poisson Gamma Belief Network
CHAOJIE WANG · Bo Chen · SUCHENG XIAO · Mingyuan Zhou
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #238
Automatic Posterior Transformation for Likelihood-Free Inference
David Greenberg · Marcel Nonnenmacher · Jakob Macke
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #239
Active Learning for Decision-Making from Imbalanced Observational Data
Iiris Sundin · Peter Schulam · Eero Siivola · Aki Vehtari · Suchi Saria · Samuel Kaski
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #240
Validating Causal Inference Models via Influence Functions
Ahmed Alaa · M van der Schaar
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #241
Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks
Charith Mendis · Alex Renda · Dr.Saman Amarasinghe · Michael Carbin
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #242
Learning to Groove with Inverse Sequence Transformations
Jon Gillick · Adam Roberts · Jesse Engel · Douglas Eck · David Bamman
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #243
Grid-Wise Control for Multi-Agent Reinforcement Learning in Video Game AI
Lei Han · Peng Sun · Yali Du · Jiechao Xiong · Qing Wang · Xinghai Sun · Han Liu · Tong Zhang
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #244
HOList: An Environment for Machine Learning of Higher Order Logic Theorem Proving
Kshitij Bansal · Sarah Loos · Markus Rabe · Christian Szegedy · Stewart Wilcox
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #245
Molecular Hypergraph Grammar with Its Application to Molecular Optimization
Hiroshi Kajino
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #246
Graph Neural Network for Music Score Data and Modeling Expressive Piano Performance
Dasaem Jeong · Taegyun Kwon · Yoojin Kim · Juhan Nam
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom
Learning to Prove Theorems via Interacting with Proof Assistants
Kaiyu Yang · Jia Deng
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #248
Circuit-GNN: Graph Neural Networks for Distributed Circuit Design
GUO ZHANG · Hao He · Dina Katabi
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #249
Learning to Optimize Multigrid PDE Solvers
Daniel Greenfeld · Meirav Galun · Ronen Basri · Irad Yavneh · Ron Kimmel
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #250
A Block Coordinate Descent Proximal Method for Simultaneous Filtering and Parameter Estimation
Ramin Raziperchikolaei · Harish Bhat
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #251
Learning Hawkes Processes Under Synchronization Noise
William Trouleau · Jalal Etesami · Matthias Grossglauser · Negar Kiyavash · Patrick Thiran
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #252
Generative Adversarial User Model for Reinforcement Learning Based Recommendation System
Xinshi Chen · Shuang Li · Hui Li · Shaohua Jiang · Yuan Qi · Le Song
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #253
A Statistical Investigation of Long Memory in Language and Music
Alexander Greaves-Tunnell · Zaid Harchaoui
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #254
Deep Factors for Forecasting
Yuyang Wang · Alex Smola · Danielle Robinson · Jan Gasthaus · Dean Foster · Tim Januschowski
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #255
Weakly-Supervised Temporal Localization via Occurrence Count Learning
Julien Schroeter · Kirill Sidorov · David Marshall
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #256
Switching Linear Dynamics for Variational Bayes Filtering
Philip Becker-Ehmck · Jan Peters · Patrick van der Smagt
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #257
Imputing Missing Events in Continuous-Time Event Streams
Hongyuan Mei · Guanghui Qin · Jason Eisner
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #258
Understanding and Controlling Memory in Recurrent Neural Networks
Doron Haviv · Alexander Rivkind · Omri Barak
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #259
Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces
Philipp Becker · Harit Pandya · Gregor Gebhardt · Cheng Zhao · C. James Taylor · Gerhard Neumann
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #260
Subspace Robust Wasserstein Distances
François-Pierre Paty · Marco Cuturi
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #261
Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models
Kaspar Märtens · Kieran Campbell · Christopher Yau
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #262
Active Manifolds: A non-linear analogue to Active Subspaces
Robert Bridges · Anthony Gruber · Christopher Felder · Miki Verma · Chelsey Hoff
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #263
Optimal Minimal Margin Maximization with Boosting
Alexander Mathiasen · Kasper Green Larsen · Allan Grønlund
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #264
Generalized Linear Rule Models
Dennis Wei · Sanjeeb Dash · Tian Gao · Oktay Gunluk
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #265
Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and Applications
Pin-Yu Chen · Lingfei Wu · Sijia Liu · Indika Rajapakse
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #266
Variational Inference for sparse network reconstruction from count data
Julien Chiquet · Stephane Robin · Mahendra Mariadassou
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #267
Simplifying Graph Convolutional Networks
Felix Wu · Amauri Souza · Tianyi Zhang · Christopher Fifty · Tao Yu · Kilian Weinberger
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #268
Robust Influence Maximization for Hyperparametric Models
Dimitrios Kalimeris · Gal Kaplun · Yaron Singer
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #269
HyperGAN: A Generative Model for Diverse, Performant Neural Networks
Neale Ratzlaff · Fuxin Li
Poster
Tue Jun 11th 06:30 -- 06:50 PM @ Pacific Ballroom #270
Rates of Convergence for Sparse Variational Gaussian Process Regression
David Burt · Carl E Rasmussen · Mark van der Wilk
Poster
Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #271
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
Francesco Locatello · Stefan Bauer · Mario Lucic · Gunnar Raetsch · Sylvain Gelly · Bernhard Schölkopf · Olivier Bachem
Invited Talk
Wed Jun 12th 09:00 -- 10:00 AM @ Hall A
The U.S. Census Bureau Tries to be a Good Data Steward in the 21st Century
John M. Abowd
Invited Talk
Wed Jun 12th 10:00 -- 10:20 AM @ Hall A
Test of Time Award
Invited Talk
Wed Jun 12th 10:00 -- 10:20 AM @ Hall A
Online Dictionary Learning for Sparse Coding
Julien Mairal · Francis Bach · Jean Ponce · Guillermo Sapiro
Break
Wed Jun 12th 10:30 -- 11:00 AM @ None
Coffee Break
Oral
Wed Jun 12th 11:00 -- 11:20 AM @ Room 101
Distribution calibration for regression
Hao Song · Tom Diethe · Meelis Kull · Peter Flach
Oral
Wed Jun 12th 11:00 -- 11:20 AM @ Seaside Ballroom
Optimal Algorithms for Lipschitz Bandits with Heavy-tailed Rewards
Shiyin Lu · Guanghui Wang · Yao Hu · Lijun Zhang
Oral
Wed Jun 12th 11:00 -- 11:20 AM @ Room 201
Exploiting Worker Correlation for Label Aggregation in Crowdsourcing
Yuan Li · Benjamin Rubinstein · Trevor Cohn
Oral
Wed Jun 12th 11:00 -- 11:20 AM @ Room 102
On the Convergence and Robustness of Adversarial Training
Yisen Wang · Xingjun Ma · James Bailey · Jinfeng Yi · Bowen Zhou · Quanquan Gu
Oral
Wed Jun 12th 11:00 -- 11:20 AM @ Hall A
Sum-of-Squares Polynomial Flow
Priyank Jaini · Kira A. Selby · Yaoliang Yu
Oral
Wed Jun 12th 11:00 -- 11:20 AM @ Hall B
Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning
Natasha Jaques · Angeliki Lazaridou · Edward Hughes · Caglar Gulcehre · Pedro Ortega · DJ Strouse · Joel Z Leibo · Nando de Freitas
Oral
Wed Jun 12th 11:00 -- 11:20 AM @ Room 103
Distributed Learning with Sublinear Communication
Jayadev Acharya · Christopher De Sa · Dylan Foster · Karthik Sridharan
Oral
Wed Jun 12th 11:00 -- 11:20 AM @ Grand Ballroom
Theoretically Principled Trade-off between Robustness and Accuracy
Hongyang Zhang · Yaodong Yu · Jiantao Jiao · Eric Xing · Laurent El Ghaoui · Michael Jordan
Oral
Wed Jun 12th 11:00 -- 11:20 AM @ Room 104
Complexity of Linear Regions in Deep Networks
Boris Hanin · David Rolnick
Oral
Wed Jun 12th 11:20 -- 11:25 AM @ Seaside Ballroom
Target Tracking for Contextual Bandits: Application to Demand Side Management
Margaux Brégère · Pierre Gaillard · Yannig Goude · Gilles Stoltz
Oral
Wed Jun 12th 11:20 -- 11:25 AM @ Room 201
Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems
Ted Meeds · Geoffrey Roeder · Paul Grant · Andrew Phillips · Neil Dalchau
Oral
Wed Jun 12th 11:20 -- 11:25 AM @ Room 103
On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimization
Hao Yu · rong jin · Sen Yang
Oral
Wed Jun 12th 11:20 -- 11:25 AM @ Grand Ballroom
The Odds are Odd: A Statistical Test for Detecting Adversarial Examples
Kevin Roth · Yannic Kilcher · Thomas Hofmann
Oral
Wed Jun 12th 11:20 -- 11:25 AM @ Hall A
FloWaveNet : A Generative Flow for Raw Audio
Sungwon Kim · Sang-gil Lee · Jongyoon Song · Jaehyeon Kim · Sungroh Yoon
Oral
Wed Jun 12th 11:20 -- 11:25 AM @ Room 102
Learning with Bad Training Data via Iterative Trimmed Loss Minimization
Yanyao Shen · Sujay Sanghavi
Oral
Wed Jun 12th 11:20 -- 11:25 AM @ Room 104
On Connected Sublevel Sets in Deep Learning
Quynh Nguyen
Oral
Wed Jun 12th 11:20 -- 11:25 AM @ Room 101
Graph Convolutional Gaussian Processes
Ian Walker · Ben Glocker
Oral
Wed Jun 12th 11:20 -- 11:25 AM @ Hall B
Maximum Entropy-Regularized Multi-Goal Reinforcement Learning
Rui Zhao · Xudong Sun · Volker Tresp
Oral
Wed Jun 12th 11:25 -- 11:30 AM @ Seaside Ballroom
Correlated bandits or: How to minimize mean-squared error online
Vinay Praneeth Boda · Prashanth L.A.
Oral
Wed Jun 12th 11:25 -- 11:30 AM @ Room 103
Stochastic Gradient Push for Distributed Deep Learning
Mahmoud Assran · Nicolas Loizou · Nicolas Ballas · Michael Rabbat
Oral
Wed Jun 12th 11:25 -- 11:30 AM @ Grand Ballroom
ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation
Yuzhe Yang · GUO ZHANG · Zhi Xu · Dina Katabi
Oral
Wed Jun 12th 11:25 -- 11:30 AM @ Hall A
Are Generative Classifiers More Robust to Adversarial Attacks?
Yingzhen Li · John Bradshaw · Yash Sharma
Oral
Wed Jun 12th 11:25 -- 11:30 AM @ Hall B
Imitating Latent Policies from Observation
Ashley Edwards · Himanshu Sahni · Yannick Schroecker · Charles Isbell
Oral
Wed Jun 12th 11:25 -- 11:30 AM @ Room 104
Adversarial Examples Are a Natural Consequence of Test Error in Noise
Justin Gilmer · Nicolas Ford · Nicholas Carlini · Ekin Cubuk
Oral
Wed Jun 12th 11:25 -- 11:30 AM @ Room 102
On discriminative learning of prediction uncertainty
Vojtech Franc · Daniel Prusa
Oral
Wed Jun 12th 11:25 -- 11:30 AM @ Room 201
A Multitask Multiple Kernel Learning Algorithm for Survival Analysis with Application to Cancer Biology
Onur Dereli · Ceyda Oğuz · Mehmet Gönen
Oral
Wed Jun 12th 11:25 -- 11:30 AM @ Room 101
Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation
Ahsan Alvi · Binxin Ru · Jan-Peter Calliess · Stephen Roberts · Michael A Osborne
Oral
Wed Jun 12th 11:30 -- 11:35 AM @ Room 201
Fast and Flexible Inference of Joint Distributions from their Marginals
Charles Frogner · Tomaso Poggio
Oral
Wed Jun 12th 11:30 -- 11:35 AM @ Room 101
GOODE: A Gaussian Off-The-Shelf Ordinary Differential Equation Solver
David John · Vincent Heuveline · Michael Schober
Oral
Wed Jun 12th 11:30 -- 11:35 AM @ Room 104
Greedy Layerwise Learning Can Scale To ImageNet
Eugene Belilovsky · Michael Eickenberg · Edouard Oyallon
Oral
Wed Jun 12th 11:30 -- 11:35 AM @ Room 102
Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels
Pengfei Chen · Ben Liao · Guangyong Chen · Shengyu Zhang
Oral
Wed Jun 12th 11:30 -- 11:35 AM @ Grand Ballroom
Certified Adversarial Robustness via Randomized Smoothing
Jeremy Cohen · Elan Rosenfeld · Zico Kolter
Oral
Wed Jun 12th 11:30 -- 11:35 AM @ Hall B
SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning
Marvin Zhang · Sharad Vikram · Laura Smith · Pieter Abbeel · Matthew Johnson · Sergey Levine
Oral
Wed Jun 12th 11:30 -- 11:35 AM @ Seaside Ballroom
Stay With Me: Lifetime Maximization Through Heteroscedastic Linear Bandits With Reneging
Ping-Chun Hsieh · Xi Liu · Anirban Bhattacharya · P R Kumar
Oral
Wed Jun 12th 11:30 -- 11:35 AM @ Hall A
A Gradual, Semi-Discrete Approach to Generative Network Training via Explicit Wasserstein Minimization
Yucheng Chen · Matus Telgarsky · Chao Zhang · Bolton Bailey · Daniel Hsu · Jian Peng
Oral
Wed Jun 12th 11:30 -- 11:35 AM @ Room 103
Collective Model Fusion for Multiple Black-Box Experts
Minh Hoang · Nghia Hoang · Bryan Kian Hsiang Low · Carleton Kingsford
Oral
Wed Jun 12th 11:35 -- 11:40 AM @ Room 102
Does Data Augmentation Lead to Positive Margin?
Shashank Rajput · Zhili Feng · Zachary Charles · Po-Ling Loh · Dimitris Papailiopoulos
Oral
Wed Jun 12th 11:35 -- 11:40 AM @ Hall A
Disentangling Disentanglement in Variational Autoencoders
Emile Mathieu · Tom Rainforth · N Siddharth · Yee Whye Teh
Oral
Wed Jun 12th 11:35 -- 11:40 AM @ Room 201
Cognitive model priors for predicting human decisions
Joshua C Peterson · David D Bourgin · Daniel Reichman · Thomas Griffiths · Stuart Russell
Oral
Wed Jun 12th 11:35 -- 11:40 AM @ Room 104
On the Impact of the Activation function on Deep Neural Networks Training
Soufiane Hayou · Arnaud Doucet · Judith Rousseau
Oral
Wed Jun 12th 11:35 -- 11:40 AM @ Room 103
Trading Redundancy for Communication: Speeding up Distributed SGD for Non-convex Optimization
Farzin Haddadpour · Mohammad Mahdi Kamani · Mehrdad Mahdavi · Viveck Cadambe
Oral
Wed Jun 12th 11:35 -- 11:40 AM @ Grand Ballroom
Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition
Yao Qin · Nicholas Carlini · Garrison Cottrell · Ian Goodfellow · Colin Raffel
Oral
Wed Jun 12th 11:35 -- 11:40 AM @ Seaside Ballroom
Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits
Branislav Kveton · Csaba Szepesvari · Sharan Vaswani · Zheng Wen · Tor Lattimore · Mohammad Ghavamzadeh
Oral
Wed Jun 12th 11:35 -- 11:40 AM @ Hall B
Dimension-Wise Importance Sampling Weight Clipping for Sample-Efficient Reinforcement Learning
Seungyul Han · Youngchul Sung
Oral
Wed Jun 12th 11:35 -- 11:40 AM @ Room 101
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models
Alessandro Davide Ialongo · Mark van der Wilk · James Hensman · Carl E Rasmussen
Oral
Wed Jun 12th 11:40 AM -- 12:00 PM @ Hall B
Structured agents for physical construction
Victor Bapst · Alvaro Sanchez · Carl Doersch · Kimberly Stachenfeld · Pushmeet Kohli · Peter Battaglia · Jessica Hamrick
Oral
Wed Jun 12th 11:40 AM -- 12:00 PM @ Room 103
Trimming the $\ell_1$ Regularizer: Statistical Analysis, Optimization, and Applications to Deep Learning
Jihun Yun · Peng Zheng · Eunho Yang · Aurelie Lozano · Aleksandr Aravkin
Oral
Wed Jun 12th 11:40 AM -- 12:00 PM @ Room 104
Estimating Information Flow in Deep Neural Networks
Ziv Goldfeld · Ewout van den Berg · Kristjan Greenewald · Igor Melnyk · Nam Nguyen · Brian Kingsbury · Yury Polyanskiy
Oral
Wed Jun 12th 11:40 AM -- 12:00 PM @ Room 102
Robust Learning from Untrusted Sources
Nikola Konstantinov · Christoph H. Lampert
Oral
Wed Jun 12th 11:40 AM -- 12:00 PM @ Hall A
EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE
Chao Ma · Sebastian Tschiatschek · Konstantina Palla · Jose Hernandez-Lobato · Sebastian Nowozin · Cheng Zhang
Oral
Wed Jun 12th 11:40 AM -- 12:00 PM @ Room 201
Conditioning by adaptive sampling for robust design
David Brookes · Jennifer Listgarten
Oral
Wed Jun 12th 11:40 AM -- 12:00 PM @ Seaside Ballroom
Beating Stochastic and Adversarial Semi-bandits Optimally and Simultaneously
Julian Zimmert · Haipeng Luo · Chen-Yu Wei
Oral
Wed Jun 12th 11:40 AM -- 12:00 PM @ Grand Ballroom
Parsimonious Black-Box Adversarial Attacks via Efficient Combinatorial Optimization
Seungyong Moon · Gaon An · Hyun Oh Song
Oral
Wed Jun 12th 11:40 AM -- 12:00 PM @ Room 101
AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEs
Gabriele Abbati · Philippe Wenk · Michael A Osborne · Andreas Krause · Bernhard Schölkopf · Stefan Bauer
Oral
Wed Jun 12th 12:00 -- 12:05 PM @ Room 103
Compressed Factorization: Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data
Vatsal Sharan · Kai Sheng Tai · Peter Bailis · Gregory Valiant
Oral
Wed Jun 12th 12:00 -- 12:05 PM @ Seaside Ballroom
Bilinear Bandits with Low-rank Structure
Kwang-Sung Jun · Rebecca Willett · Stephen Wright · Robert Nowak
Oral
Wed Jun 12th 12:00 -- 12:05 PM @ Grand Ballroom
Wasserstein Adversarial Examples via Projected Sinkhorn Iterations
Eric Wong · Frank Schmidt · Zico Kolter
Oral
Wed Jun 12th 12:00 -- 12:05 PM @ Hall A
A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning
Yoshihiro Nagano · Shoichiro Yamaguchi · Yasuhiro Fujita · Masanori Koyama
Oral
Wed Jun 12th 12:00 -- 12:05 PM @ Room 101
End-to-End Probabilistic Inference for Nonstationary Audio Analysis
William Wilkinson · Michael Riis Andersen · Joshua D. Reiss · Dan Stowell · Arno Solin
Oral
Wed Jun 12th 12:00 -- 12:05 PM @ Hall B
Learning Novel Policies For Tasks
Yunbo Zhang · Wenhao Yu · Greg Turk
Oral
Wed Jun 12th 12:00 -- 12:05 PM @ Room 201
Direct Uncertainty Prediction for Medical Second Opinions
Maithra Raghu · Katy Blumer · Rory sayres · Ziad Obermeyer · Bobby Kleinberg · Sendhil Mullainathan · Jon Kleinberg
Oral
Wed Jun 12th 12:00 -- 12:05 PM @ Room 102
SELFIE: Refurbishing Unclean Samples for Robust Deep Learning
Hwanjun Song · Minseok Kim · Jae-Gil Lee
Oral
Wed Jun 12th 12:00 -- 12:05 PM @ Room 104
The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Sharp Minima and Regularization Effects
Zhanxing Zhu · Jingfeng Wu · Bing Yu · Lei Wu · Jinwen Ma
Oral
Wed Jun 12th 12:05 -- 12:10 PM @ Seaside Ballroom
Online Learning to Rank with Features
Shuai Li · Tor Lattimore · Csaba Szepesvari
Oral
Wed Jun 12th 12:05 -- 12:10 PM @ Room 104
Characterizing Well-Behaved vs. Pathological Deep Neural Networks
Antoine Labatie
Oral
Wed Jun 12th 12:05 -- 12:10 PM @ Room 103
Noisy Dual Principal Component Pursuit
Tianyu Ding · Zhihui Zhu · Tianjiao Ding · Yunchen Yang · Daniel Robinson · Manolis Tsakiris · Rene Vidal
Oral
Wed Jun 12th 12:05 -- 12:10 PM @ Room 101
Deep Gaussian Processes with Importance-Weighted Variational Inference
Hugh Salimbeni · Vincent Dutordoir · James Hensman · Marc P Deisenroth
Oral
Wed Jun 12th 12:05 -- 12:10 PM @ Room 102
Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerance
Cong Xie · Sanmi Koyejo · Indranil Gupta
Oral
Wed Jun 12th 12:05 -- 12:10 PM @ Hall B
Taming MAML: Efficient unbiased meta-reinforcement learning
Hao Liu · Richard Socher · Caiming Xiong
Oral
Wed Jun 12th 12:05 -- 12:10 PM @ Hall A
Emerging Convolutions for Generative Normalizing Flows
Emiel Hoogeboom · Rianne Van den Berg · Max Welling
Oral
Wed Jun 12th 12:05 -- 12:10 PM @ Room 201
Dynamic Measurement Scheduling for Event Forecasting using Deep RL
Chun-Hao (Kingsley) Chang · Mingjie Mai · Anna Goldenberg
Oral
Wed Jun 12th 12:05 -- 12:10 PM @ Grand Ballroom
Transferable Clean-Label Poisoning Attacks on Deep Neural Nets
Chen Zhu · W. Ronny Huang · Hengduo Li · Gavin Taylor · Christoph Studer · Tom Goldstein
Oral
Wed Jun 12th 12:10 -- 12:15 PM @ Hall A
A Large-Scale Study on Regularization and Normalization in GANs
Karol Kurach · Mario Lucic · Xiaohua Zhai · Marcin Michalski · Sylvain Gelly
Oral
Wed Jun 12th 12:10 -- 12:15 PM @ Room 101
Automated Model Selection with Bayesian Quadrature
Henry Chai · Jean-Francois Ton · Michael A Osborne · Roman Garnett
Oral
Wed Jun 12th 12:10 -- 12:15 PM @ Grand Ballroom
NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks
Yandong li · Lijun Li · Liqiang Wang · Tong Zhang · Boqing Gong
Oral
Wed Jun 12th 12:10 -- 12:15 PM @ Room 104
Understanding Geometry of Encoder-Decoder CNNs
Jong Chul C Ye · woonkyoung Sung
Oral
Wed Jun 12th 12:10 -- 12:15 PM @ Room 102
Concentration Inequalities for Conditional Value at Risk
Philip Thomas · Erik Learned-Miller
Oral
Wed Jun 12th 12:10 -- 12:15 PM @ Room 103
Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling
Shanshan Wu · Alexandros Dimakis · Sujay Sanghavi · Felix Xinnan Yu · Daniel Holtmann-Rice · Dmitry Storcheus · Afshin Rostamizadeh · Sanjiv Kumar
Oral
Wed Jun 12th 12:10 -- 12:15 PM @ Hall B
Self-Supervised Exploration via Disagreement
Deepak Pathak · Dhiraj Gandhi · Abhinav Gupta
Oral
Wed Jun 12th 12:10 -- 12:15 PM @ Seaside Ballroom
On the Design of Estimators for Bandit Off-Policy Evaluation
Nikos Vlassis · Aurelien Bibaut · Maria Dimakopoulou · Tony Jebara
Oral
Wed Jun 12th 12:10 -- 12:15 PM @ Room 201
Parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization
Hesham Mostafa · Xin Wang
Oral
Wed Jun 12th 12:15 -- 12:20 PM @ Room 102
Data Poisoning Attacks in Multi-Party Learning
Saeed Mahloujifar · Mohammad Mahmoody · Ameer Mohammed
Oral
Wed Jun 12th 12:15 -- 12:20 PM @ Hall A
Variational Annealing of GANs: A Langevin Perspective
Chenyang Tao · Shuyang Dai · Liqun Chen · Ke Bai · Junya Chen · Chang Liu · RUIYI (ROY) ZHANG · Georgiy Bobashev · Lawrence Carin
Oral
Wed Jun 12th 12:15 -- 12:20 PM @ Room 201
DeepNose: Using artificial neural networks to represent the space of odorants
Ngoc Tran · Daniel Kepple · Sergey Shuvaev · Alexei Koulakov
Oral
Wed Jun 12th 12:15 -- 12:20 PM @ Room 104
Traditional and Heavy Tailed Self Regularization in Neural Network Models
Michael Mahoney · Charles H Martin
Oral
Wed Jun 12th 12:15 -- 12:20 PM @ Grand Ballroom
Simple Black-box Adversarial Attacks
Chuan Guo · Jacob Gardner · Yurong You · Andrew Wilson · Kilian Weinberger
Oral
Wed Jun 12th 12:15 -- 12:20 PM @ Seaside Ballroom
Dynamic Learning with Frequent New Product Launches: A Sequential Multinomial Logit Bandit Problem
Junyu Cao · Wei Sun
Oral
Wed Jun 12th 12:15 -- 12:20 PM @ Room 103
Screening rules for Lasso with non-convex Sparse Regularizers
alain rakotomamonjy · Gilles Gasso · Joseph Salmon
Oral
Wed Jun 12th 12:15 -- 12:20 PM @ Hall B
Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables
Kate Rakelly · Aurick Zhou · Chelsea Finn · Sergey Levine · Deirdre Quillen
Break
Wed Jun 12th 12:30 -- 02:00 PM @ None
Lunch - on your own
Oral
Wed Jun 12th 02:00 -- 02:20 PM @ Room 102
Distributed Weighted Matching via Randomized Composable Coresets
Sepehr Assadi · Mohammad Hossein Bateni · Vahab Mirrokni
Oral
Wed Jun 12th 02:00 -- 02:20 PM @ Grand Ballroom
Causal Identification under Markov Equivalence: Completeness Results
Amin Jaber · Jiji Zhang · Elias Bareinboim
Oral
Wed Jun 12th 02:00 -- 02:20 PM @ Hall B
The Natural Language of Actions
Guy Tennenholtz · Shie Mannor
Oral
Wed Jun 12th 02:00 -- 02:20 PM @ Seaside Ballroom
Context-Aware Zero-Shot Learning for Object Recognition
Eloi Zablocki · Patrick Bordes · Laure Soulier · Benjamin Piwowarski · Patrick Gallinari
Oral
Wed Jun 12th 02:00 -- 02:20 PM @ Room 101
Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with double power-law behavior
Fadhel Ayed · Juho Lee · Francois Caron
Oral
Wed Jun 12th 02:00 -- 02:20 PM @ Room 104
Almost surely constrained convex optimization
Olivier Fercoq · Ahmet Alacaoglu · Ion Necoara · Volkan Cevher
Oral
Wed Jun 12th 02:00 -- 02:20 PM @ Hall A
Invertible Residual Networks
Jens Behrmann · Will Grathwohl · Tian Qi Chen · David Duvenaud · Joern-Henrik Jacobsen
Oral
Wed Jun 12th 02:00 -- 02:20 PM @ Room 201
Domain Agnostic Learning with Disentangled Representations
Xingchao Peng · Zijun Huang · Ximeng Sun · Kate Saenko
Oral
Wed Jun 12th 02:00 -- 02:20 PM @ Room 103
Monge blunts Bayes: Hardness Results for Adversarial Training
Zac Cranko · Aditya Menon · Richard Nock · Cheng Soon Ong · Zhan Shi · Christian Walder
Oral
Wed Jun 12th 02:20 -- 02:25 PM @ Room 104
Generalized Majorization-Minimization
Sobhan Naderi Parizi · Kun He · Reza Aghajani · Stan Sclaroff · Pedro Felzenszwalb
Oral
Wed Jun 12th 02:20 -- 02:25 PM @ Hall A
NAS-Bench-101: Towards Reproducible Neural Architecture Search
Chris Ying · Aaron Klein · Eric Christiansen · Esteban Real · Kevin Murphy · Frank Hutter
Oral
Wed Jun 12th 02:20 -- 02:25 PM @ Seaside Ballroom
Band-limited Training and Inference for Convolutional Neural Networks
Adam Dziedzic · John Paparrizos · Sanjay Krishnan · Aaron Elmore · Michael Franklin
Oral
Wed Jun 12th 02:20 -- 02:25 PM @ Room 101
DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures
Andrew R Lawrence · Carl Henrik Ek · Neill Campbell
Oral
Wed Jun 12th 02:20 -- 02:25 PM @ Room 201
Composing Value Functions in Reinforcement Learning
Benjamin van Niekerk · Steven James · Adam Earle · Benjamin Rosman
Oral
Wed Jun 12th 02:20 -- 02:25 PM @ Room 103
Better generalization with less data using robust gradient descent
Matthew Holland · Kazushi Ikeda
Oral
Wed Jun 12th 02:20 -- 02:25 PM @ Grand Ballroom
Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models
Michael Oberst · David Sontag
Oral
Wed Jun 12th 02:20 -- 02:25 PM @ Room 102
Multivariate Submodular Optimization
Richard Santiago · F. Bruce Shepherd
Oral
Wed Jun 12th 02:20 -- 02:25 PM @ Hall B
Control Regularization for Reduced Variance Reinforcement Learning
Richard Cheng · Abhinav Verma · Gabor Orosz · Swarat Chaudhuri · Yisong Yue · Joel Burdick
Oral
Wed Jun 12th 02:25 -- 02:30 PM @ Room 201
Fast Context Adaptation via Meta-Learning
Luisa Zintgraf · Kyriacos Shiarlis · Vitaly Kurin · Katja Hofmann · Shimon Whiteson
Oral
Wed Jun 12th 02:25 -- 02:30 PM @ Seaside Ballroom
Learning Classifiers for Target Domain with Limited or No Labels
Pengkai Zhu · Hanxiao Wang · Venkatesh Saligrama
Oral
Wed Jun 12th 02:25 -- 02:30 PM @ Room 102
Beyond Adaptive Submodularity: Approximation Guarantees of Greedy Policy with Adaptive Submodularity Ratio
Kaito Fujii · Shinsaku Sakaue
Oral
Wed Jun 12th 02:25 -- 02:30 PM @ Room 104
On the Computation and Communication Complexity of Parallel SGD with Dynamic Batch Sizes for Stochastic Non-Convex Optimization
Hao Yu · rong jin
Oral
Wed Jun 12th 02:25 -- 02:30 PM @ Room 103
Near optimal finite time identification of arbitrary linear dynamical systems
Tuhin Sarkar · Alexander Rakhlin
Oral
Wed Jun 12th 02:25 -- 02:30 PM @ Hall A
Approximated Oracle Filter Pruning for Destructive CNN Width Optimization
XIAOHAN DING · guiguang ding · Yuchen Guo · Jungong Han · Chenggang Yan
Oral
Wed Jun 12th 02:25 -- 02:30 PM @ Hall B
On the Generalization Gap in Reparameterizable Reinforcement Learning
Huan Wang · Stephan Zheng · Caiming Xiong · Richard Socher
Oral
Wed Jun 12th 02:25 -- 02:30 PM @ Room 101
Random Function Priors for Correlation Modeling
Aonan Zhang · John Paisley
Oral
Wed Jun 12th 02:25 -- 02:30 PM @ Grand Ballroom
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models
Biwei Huang · Kun Zhang · Mingming Gong · Clark Glymour
Oral
Wed Jun 12th 02:30 -- 02:35 PM @ Room 104
Simple Stochastic Gradient Methods for Non-Smooth Non-Convex Regularized Optimization
Michael Metel · Akiko Takeda
Oral
Wed Jun 12th 02:30 -- 02:35 PM @ Room 102
Approximating Orthogonal Matrices with Effective Givens Factorization
Thomas Frerix · Joan Bruna
Oral
Wed Jun 12th 02:30 -- 02:35 PM @ Room 103
Lossless or Quantized Boosting with Integer Arithmetic
Richard Nock · Robert C Williamson
Oral
Wed Jun 12th 02:30 -- 02:35 PM @ Hall B
Trajectory-Based Off-Policy Deep Reinforcement Learning
Andreas Doerr · Michael Volpp · Marc Toussaint · Sebastian Trimpe · Christian Daniel
Oral
Wed Jun 12th 02:30 -- 02:35 PM @ Room 201
Provable Guarantees for Gradient-Based Meta-Learning
Nina Balcan · Mikhail Khodak · Ameet Talwalkar
Oral
Wed Jun 12th 02:30 -- 02:35 PM @ Grand Ballroom
Classifying Treatment Responders Under Causal Effect Monotonicity
Nathan Kallus
Oral
Wed Jun 12th 02:30 -- 02:35 PM @ Room 101
Variational Russian Roulette for Deep Bayesian Nonparametrics
Kai Xu · Akash Srivastava · Charles Sutton
Oral
Wed Jun 12th 02:30 -- 02:35 PM @ Seaside Ballroom
Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules
Daniel Ho · Eric Liang · Peter Chen · Ion Stoica · Pieter Abbeel
Oral
Wed Jun 12th 02:30 -- 02:35 PM @ Hall A
LegoNet: Efficient Convolutional Neural Networks with Lego Filters
Zhaohui Yang · Yunhe Wang · Chuanjian Liu · Hanting Chen · Chunjing Xu · Boxin Shi · Chao Xu · Chang Xu
Oral
Wed Jun 12th 02:35 -- 02:40 PM @ Room 104
Surrogate Losses for Online Learning of Stepsizes in Stochastic Non-Convex Optimization
zhenxun zhuang · Ashok Cutkosky · Francesco Orabona
Oral
Wed Jun 12th 02:35 -- 02:40 PM @ Hall A
Sorting Out Lipschitz Function Approximation
Cem Anil · James Lucas · Roger Grosse
Oral
Wed Jun 12th 02:35 -- 02:40 PM @ Room 101
Incorporating Grouping Information into Bayesian Decision Tree Ensembles
JUNLIANG DU · Antonio Linero
Oral
Wed Jun 12th 02:35 -- 02:40 PM @ Room 102
New results on information theoretic clustering
Ferdinando Cicalese · Eduardo Laber · Lucas Murtinho
Oral
Wed Jun 12th 02:35 -- 02:40 PM @ Room 201
Towards Understanding Knowledge Distillation
Mary Phuong · Christoph H. Lampert
Oral
Wed Jun 12th 02:35 -- 02:40 PM @ Room 103
Orthogonal Random Forest for Causal Inference
Miruna Oprescu · Vasilis Syrgkanis · Steven Wu
Oral
Wed Jun 12th 02:35 -- 02:40 PM @ Hall B
A Deep Reinforcement Learning Perspective on Internet Congestion Control
Nathan Jay · Noga H. Rotman · Brighten Godfrey · Michael Schapira · Aviv Tamar
Oral
Wed Jun 12th 02:35 -- 02:40 PM @ Grand Ballroom
Learning Models from Data with Measurement Error: Tackling Underreporting
Roy Adams · Yuelong Ji · Xiaobin Wang · Suchi Saria
Oral
Wed Jun 12th 02:35 -- 02:40 PM @ Seaside Ballroom
Anomaly Detection With Multiple-Hypotheses Predictions
Duc Tam Nguyen · Zhongyu Lou · Michael Klar · Thomas Brox
Oral
Wed Jun 12th 02:40 -- 03:00 PM @ Room 102
Improved Parallel Algorithms for Density-Based Network Clustering
Mohsen Ghaffari · Silvio Lattanzi · Slobodan Mitrović
Oral
Wed Jun 12th 02:40 -- 03:00 PM @ Hall A
Graph Element Networks: adaptive, structured computation and memory
Ferran Alet · Adarsh Keshav Jeewajee · Maria Bauza Villalonga · Alberto Rodriguez · Tomas Lozano-Perez · Leslie Kaelbling
Oral
Wed Jun 12th 02:40 -- 03:00 PM @ Room 104
Efficient Dictionary Learning with Gradient Descent
Dar Gilboa · Sam Buchanan · John Wright
Oral
Wed Jun 12th 02:40 -- 03:00 PM @ Room 101
Variational Implicit Processes
Chao Ma · Yingzhen Li · Jose Hernandez-Lobato
Oral
Wed Jun 12th 02:40 -- 03:00 PM @ Hall B
Model-Based Active Exploration
Pranav Shyam · Wojciech Jaśkowski · Faustino Gomez
Oral
Wed Jun 12th 02:40 -- 03:00 PM @ Seaside Ballroom
Kernel Mean Matching for Content Addressability of GANs
Wittawat Jitkrittum · Wittawat Jitkrittum · Patsorn Sangkloy · Patsorn Sangkloy · Muhammad Waleed Gondal · Muhammad Waleed Gondal · Amit Raj · Amit Raj · James Hays · James Hays · Bernhard Schölkopf · Bernhard Schölkopf
Oral
Wed Jun 12th 02:40 -- 03:00 PM @ Grand Ballroom
Adjustment Criteria for Generalizing Experimental Findings
Juan Correa · Jin Tian · Elias Bareinboim
Oral
Wed Jun 12th 02:40 -- 03:00 PM @ Room 103
MONK -- Outlier-Robust Mean Embedding Estimation by Median-of-Means
Matthieu Lerasle · Zoltan Szabo · Timothée Mathieu · Guillaume Lecue
Oral
Wed Jun 12th 02:40 -- 03:00 PM @ Room 201
Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers
Hong Liu · Mingsheng Long · Jianmin Wang · Michael Jordan
Oral
Wed Jun 12th 03:00 -- 03:05 PM @ Room 104
Plug-and-Play Methods Provably Converge with Properly Trained Denoisers
Ernest Ryu · Jialin Liu · Sicheng Wang · Xiaohan Chen · Zhangyang Wang · Wotao Yin
Oral
Wed Jun 12th 03:00 -- 03:05 PM @ Hall B
Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations
Daniel Brown · Wonjoon Goo · Prabhat Nagarajan · Scott Niekum
Oral
Wed Jun 12th 03:00 -- 03:05 PM @ Seaside Ballroom
Neural Inverse Knitting: From Images to Manufacturing Instructions
Alexandre Kaspar · Tae-Hyun Oh · Liane Makatura · Petr Kellnhofer · Wojciech Matusik
Oral
Wed Jun 12th 03:00 -- 03:05 PM @ Room 102
Submodular Observation Selection and Information Gathering for Quadratic Models
Abolfazl Hashemi · Mahsa Ghasemi · Haris Vikalo · Ufuk Topcu
Oral
Wed Jun 12th 03:00 -- 03:05 PM @ Hall A
Training CNNs with Selective Allocation of Channels
Jongheon Jeong · Jinwoo Shin
Oral
Wed Jun 12th 03:00 -- 03:05 PM @ Grand Ballroom
Conditional Independence in Testing Bayesian Networks
Yujia Shen · Haiying Huang · Arthur Choi · Adnan Darwiche
Oral
Wed Jun 12th 03:00 -- 03:05 PM @ Room 103
The advantages of multiple classes for reducing overfitting from test set reuse
Vitaly Feldman · Roy Frostig · Moritz Hardt
Oral
Wed Jun 12th 03:00 -- 03:05 PM @ Room 201
Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain Adaptation
Xinyang Chen · Sinan Wang · Mingsheng Long · Jianmin Wang
Oral
Wed Jun 12th 03:00 -- 03:05 PM @ Room 101
Discovering Latent Covariance Structures for Multiple Time Series
Anh Tong · Jaesik Choi
Oral
Wed Jun 12th 03:05 -- 03:10 PM @ Hall A
Equivariant Transformer Networks
Kai Sheng Tai · Peter Bailis · Gregory Valiant
Oral
Wed Jun 12th 03:05 -- 03:10 PM @ Room 103
On the statistical rate of nonlinear recovery in generative models with heavy-tailed data
Xiaohan Wei · Zhuoran Yang · Zhaoran Wang
Oral
Wed Jun 12th 03:05 -- 03:10 PM @ Grand Ballroom
Sensitivity Analysis of Linear Structural Causal Models
Carlos Cinelli · Daniel Kumor · Bryant Chen · Judea Pearl · Elias Bareinboim
Oral
Wed Jun 12th 03:05 -- 03:10 PM @ Hall B
Distributional Multivariate Policy Evaluation and Exploration with the Bellman GAN
dror freirich · Tzahi Shimkin · Ron Meir · Aviv Tamar
Oral
Wed Jun 12th 03:05 -- 03:10 PM @ Room 201
Learning-to-Learn Stochastic Gradient Descent with Biased Regularization
Giulia Denevi · Carlo Ciliberto · Riccardo Grazzi · Massimiliano Pontil
Oral
Wed Jun 12th 03:05 -- 03:10 PM @ Room 101
Scalable Training of Inference Networks for Gaussian-Process Models
Jiaxin Shi · Mohammad Emtiyaz Khan · Jun Zhu
Oral
Wed Jun 12th 03:05 -- 03:10 PM @ Seaside Ballroom
Making Convolutional Networks Shift-Invariant Again
Richard Zhang
Oral
Wed Jun 12th 03:05 -- 03:10 PM @ Room 104
Riemannian adaptive stochastic gradient algorithms on matrix manifolds
Hiroyuki Kasai · Pratik Kumar Jawanpuria · Bamdev Mishra
Oral
Wed Jun 12th 03:05 -- 03:10 PM @ Room 102
Submodular Cost Submodular Cover with an Approximate Oracle
Victoria Crawford · Alan Kuhnle · My T Thai
Oral
Wed Jun 12th 03:10 -- 03:15 PM @ Seaside Ballroom
Generative Modeling of Infinite Occluded Objects for Compositional Scene Representation
Jinyang Yuan · Bin Li · Xiangyang Xue
Oral
Wed Jun 12th 03:10 -- 03:15 PM @ Room 104
Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic Convergence
Yi Xu · Qi Qi · Qihang Lin · rong jin · Tianbao Yang
Oral
Wed Jun 12th 03:10 -- 03:15 PM @ Room 201
BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning
Asa Cooper Stickland · Iain Murray
Oral
Wed Jun 12th 03:10 -- 03:15 PM @ Room 102
Submodular Streaming in All Its Glory: Tight Approximation, Minimum Memory and Low Adaptive Complexity
Ehsan Kazemi · Marko Mitrovic · Morteza Zadimoghaddam · Silvio Lattanzi · Amin Karbasi
Oral
Wed Jun 12th 03:10 -- 03:15 PM @ Hall B
A Baseline for Any Order Gradient Estimation in Stochastic Computation Graphs
Jingkai Mao · Jakob Foerster · Tim Rocktäschel · Maruan Al-Shedivat · Gregory Farquhar · Shimon Whiteson
Oral
Wed Jun 12th 03:10 -- 03:15 PM @ Hall A
Overcoming Multi-model Forgetting
Yassine Benyahia · Kaicheng Yu · Kamil Bennani-Smires · Martin Jaggi · Anthony C. Davison · Mathieu Salzmann · Claudiu Musat
Oral
Wed Jun 12th 03:10 -- 03:15 PM @ Grand Ballroom
More Efficient Off-Policy Evaluation through Regularized Targeted Learning
Aurelien Bibaut · Ivana Malenica · Nikos Vlassis · Mark van der Laan
Oral
Wed Jun 12th 03:10 -- 03:15 PM @ Room 103
Phase transition in PCA with missing data: Reduced signal-to-noise ratio, not sample size!
Niels Ipsen · Lars Kai Hansen
Oral
Wed Jun 12th 03:10 -- 03:15 PM @ Room 101
Bayesian Optimization Meets Bayesian Optimal Stopping
Zhongxiang Dai · Haibin Yu · Bryan Kian Hsiang Low · Patrick Jaillet
Oral
Wed Jun 12th 03:15 -- 03:20 PM @ Room 102
Hiring Under Uncertainty
Manish Purohit · Sreenivas Gollapudi · Manish Raghavan
Oral
Wed Jun 12th 03:15 -- 03:20 PM @ Room 201
Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation
Kaichao You · Ximei Wang · Mingsheng Long · Michael Jordan
Oral
Wed Jun 12th 03:15 -- 03:20 PM @ Seaside Ballroom
IMEXnet - A Forward Stable Deep Neural Network
Eldad Haber · Keegan Lensink · Eran Treister · Lars Ruthotto
Oral
Wed Jun 12th 03:15 -- 03:20 PM @ Hall A
Bayesian Nonparametric Federated Learning of Neural Networks
Mikhail Yurochkin · Mayank Agarwal · Soumya Ghosh · Kristjan Greenewald · Nghia Hoang · Yasaman Khazaeni
Oral
Wed Jun 12th 03:15 -- 03:20 PM @ Room 104
Alternating Minimizations Converge to Second-Order Optimal Solutions
Qiuwei Li · Zhihui Zhu · Gongguo Tang
Oral
Wed Jun 12th 03:15 -- 03:20 PM @ Hall B
Remember and Forget for Experience Replay
Guido Novati · Petros Koumoutsakos
Oral
Wed Jun 12th 03:15 -- 03:20 PM @ Room 103
On Medians of (Randomized) Pairwise Means
Stephan Clemencon · Pierre Laforgue · Patrice Bertail
Oral
Wed Jun 12th 03:15 -- 03:20 PM @ Grand Ballroom
Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding
Muhammad Osama · Dave Zachariah · Thomas Schön
Oral
Wed Jun 12th 03:15 -- 03:20 PM @ Room 101
Learning interpretable continuous-time models of latent stochastic dynamical systems
Lea Duncker · Gergo Bohner · Julien Boussard · Maneesh Sahani
Break
Wed Jun 12th 03:30 -- 04:00 PM @ None
Coffee Break
Oral
Wed Jun 12th 04:00 -- 04:20 PM @ Hall B
Tensor Variable Elimination for Plated Factor Graphs
Fritz Obermeyer · Elias Bingham · Martin Jankowiak · Neeraj Pradhan · Justin Chiu · Alexander Rush · Noah Goodman
Oral
Wed Jun 12th 04:00 -- 04:20 PM @ Room 101
A Tree-Based Method for Fast Repeated Sampling of Determinantal Point Processes
Jennifer Gillenwater · Alex Kulesza · Zelda Mariet · Sergei Vassilvitskii
Oral
Wed Jun 12th 04:00 -- 04:20 PM @ Hall A
How does Disagreement Help Generalization against Label Corruption?
Xingrui Yu · Bo Han · Jiangchao Yao · Gang Niu · Ivor Tsang · Masashi Sugiyama
Oral
Wed Jun 12th 04:00 -- 04:20 PM @ Room 102
Position-aware Graph Neural Networks
Jiaxuan You · Rex (Zhitao) Ying · Jure Leskovec
Oral
Wed Jun 12th 04:00 -- 04:20 PM @ Room 201
Active Embedding Search via Noisy Paired Comparisons
Gregory Canal · Andy Massimino · Mark Davenport · Christopher Rozell
Oral
Wed Jun 12th 04:00 -- 04:20 PM @ Room 103
Accelerated Linear Convergence of Stochastic Momentum Methods in Wasserstein Distances
Bugra Can · Mert Gurbuzbalaban · Lingjiong Zhu
Oral
Wed Jun 12th 04:00 -- 04:20 PM @ Room 104
Provably Efficient Imitation Learning from Observation Alone
Wen Sun · Anirudh Vemula · Byron Boots · Drew Bagnell
Oral
Wed Jun 12th 04:00 -- 04:20 PM @ Grand Ballroom
Adversarially Learned Representations for Information Obfuscation and Inference
Martin A Bertran · Natalia Martinez · Afroditi Papadaki · Qiang Qiu · Miguel Rodrigues · Galen Reeves · Guillermo Sapiro
Oral
Wed Jun 12th 04:00 -- 04:20 PM @ Seaside Ballroom
Do ImageNet Classifiers Generalize to ImageNet?
Benjamin Recht · Rebecca Roelofs · Ludwig Schmidt · Vaishaal Shankar
Oral
Wed Jun 12th 04:20 -- 04:25 PM @ Room 103
SGD without Replacement: Sharper Rates for General Smooth Convex Functions
Dheeraj Nagaraj · Prateek Jain · Praneeth Netrapalli
Oral
Wed Jun 12th 04:20 -- 04:25 PM @ Room 101
Nonlinear Stein Variational Gradient Descent for Learning Diversified Mixture Models
Dilin Wang · Qiang Liu
Oral
Wed Jun 12th 04:20 -- 04:25 PM @ Hall B
Predicate Exchange: Inference with Declarative Knowledge
Zenna Tavares · Javier Burroni · Edgar Minasyan · Armando Solar-Lezama · Rajesh Ranganath
Oral
Wed Jun 12th 04:20 -- 04:25 PM @ Hall A
EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis
Chaoqi Wang · Roger Grosse · Sanja Fidler · Guodong Zhang
Oral
Wed Jun 12th 04:20 -- 04:25 PM @ Room 102
Detecting Overlapping and Correlated Communities without Pure Nodes: Identifiability and Algorithm
Kejun Huang · Xiao Fu
Oral
Wed Jun 12th 04:20 -- 04:25 PM @ Room 104
Dead-ends and Secure Exploration in Reinforcement Learning
Mehdi Fatemi · Shikhar Sharma · Harm van Seijen · Samira Ebrahimi Kahou
Oral
Wed Jun 12th 04:20 -- 04:25 PM @ Room 201
Fast Direct Search in an Optimally Compressed Continuous Target Space for Efficient Multi-Label Active Learning
weishi shi · Qi Yu
Oral
Wed Jun 12th 04:20 -- 04:25 PM @ Seaside Ballroom
Exploring the Landscape of Spatial Robustness
Logan Engstrom · Brandon Tran · Dimitris Tsipras · Ludwig Schmidt · Aleksander Madry
Oral
Wed Jun 12th 04:20 -- 04:25 PM @ Grand Ballroom
Adaptive Neural Trees
Ryutaro Tanno · Kai Arulkumaran · Daniel Alexander · Antonio Criminisi · Aditya Nori
Oral
Wed Jun 12th 04:25 -- 04:30 PM @ Room 104
Statistics and Samples in Distributional Reinforcement Learning
Mark Rowland · Robert Dadashi · Saurabh Kumar · Remi Munos · Marc Bellemare · Will Dabney
Oral
Wed Jun 12th 04:25 -- 04:30 PM @ Hall A
Addressing the Loss-Metric Mismatch with Adaptive Loss Alignment
Chen Huang · Shuangfei Zhai · Walter Talbott · Miguel Bautista Martin · Shih-Yu Sun · Carlos Guestrin · Joshua M Susskind
Oral
Wed Jun 12th 04:25 -- 04:30 PM @ Hall B
Discriminative Regularization for Latent Variable Models with Applications to Electrocardiography
Andrew Miller · Ziad Obermeyer · John Cunningham · Sendhil Mullainathan
Oral
Wed Jun 12th 04:25 -- 04:30 PM @ Room 102
Learning Generative Models across Incomparable Spaces
Charlotte Bunne · David Alvarez-Melis · Andreas Krause · Stefanie Jegelka
Oral
Wed Jun 12th 04:25 -- 04:30 PM @ Seaside Ballroom
Sever: A Robust Meta-Algorithm for Stochastic Optimization
Ilias Diakonikolas · Gautam Kamath · Daniel Kane · Jerry Li · Jacob Steinhardt · Alistair Stewart
Oral
Wed Jun 12th 04:25 -- 04:30 PM @ Room 103
On the Complexity of Approximating Wasserstein Barycenters
Alexey Kroshnin · Nazarii Tupitsa · Darina Dvinskikh · Pavel Dvurechenskii · Alexander Gasnikov · Cesar Uribe
Oral
Wed Jun 12th 04:25 -- 04:30 PM @ Grand Ballroom
Connectivity-Optimized Representation Learning via Persistent Homology
Christoph Hofer · Roland Kwitt · Marc Niethammer · Mandar Dixit
Oral
Wed Jun 12th 04:25 -- 04:30 PM @ Room 201
Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments
Kirthevasan Kandasamy · Willie Neiswanger · Reed Zhang · Akshay Krishnamurthy · Jeff Schneider · Barnabás Póczos
Oral
Wed Jun 12th 04:25 -- 04:30 PM @ Room 101
Understanding and Accelerating Particle-Based Variational Inference
Chang Liu · Jingwei Zhuo · Pengyu Cheng · RUIYI (ROY) ZHANG · Jun Zhu
Oral
Wed Jun 12th 04:30 -- 04:35 PM @ Seaside Ballroom
Analyzing Federated Learning through an Adversarial Lens
Arjun Nitin Bhagoji · Supriyo Chakraborty · Prateek Mittal · Seraphin Calo
Oral
Wed Jun 12th 04:30 -- 04:35 PM @ Hall B
Hierarchical Decompositional Mixtures of Variational Autoencoders
Ping Liang Tan · Robert Peharz
Oral
Wed Jun 12th 04:30 -- 04:35 PM @ Hall A
Deep Compressed Sensing
Yan Wu · Mihaela Rosca · Timothy Lillicrap
Oral
Wed Jun 12th 04:30 -- 04:35 PM @ Grand Ballroom
Minimal Achievable Sufficient Statistic Learning
Milan Cvitkovic · Günther Koliander
Oral
Wed Jun 12th 04:30 -- 04:35 PM @ Room 102
Relational Pooling for Graph Representations
Ryan Murphy · Balasubramaniam Srinivasan · Vinayak A Rao · Bruno Ribeiro
Oral
Wed Jun 12th 04:30 -- 04:35 PM @ Room 103
Estimate Sequences for Variance-Reduced Stochastic Composite Optimization
Andrei Kulunchakov · Julien Mairal
Oral
Wed Jun 12th 04:30 -- 04:35 PM @ Room 101
Efficient learning of smooth probability functions from Bernoulli tests with guarantees
Paul Rolland · Ali Kavis · Alexander Niklaus Immer · Adish Singla · Volkan Cevher
Oral
Wed Jun 12th 04:30 -- 04:35 PM @ Room 104
Hessian Aided Policy Gradient
Zebang Shen · Alejandro Ribeiro · Hamed Hassani · Hui Qian · Chao Mi
Oral
Wed Jun 12th 04:30 -- 04:35 PM @ Room 201
Bayesian Generative Active Deep Learning
Toan Tran · Thanh-Toan Do · Ian Reid · Gustavo Carneiro
Oral
Wed Jun 12th 04:35 -- 04:40 PM @ Room 201
Active Learning for Probabilistic Structured Prediction of Cuts and Matchings
Sima Behpour · Anqi Liu · Brian Ziebart
Oral
Wed Jun 12th 04:35 -- 04:40 PM @ Grand Ballroom
Learning to Route in Similarity Graphs
Dmitry Baranchuk · Dmitry Persiyanov · Anton Sinitsin · Artem Babenko
Oral
Wed Jun 12th 04:35 -- 04:40 PM @ Hall B
Finding Mixed Nash Equil