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Talk
Tue Jun 11 08:45 AM -- 09:00 AM (PDT) @ Hall A
Opening Remarks
Kamalika Chaudhuri · Ruslan Salakhutdinov
[ Video] 
Invited Talk
Tue Jun 11 09:00 AM -- 10:00 AM (PDT) @ Hall A
Machine learning for robots to think fast
Aude Billard
[ Video] 
Invited Talk
Tue Jun 11 10:00 AM -- 10:20 AM (PDT) @ Hall A
Best Paper
[ Video] 
Oral
Tue Jun 11 10:00 AM -- 10:20 AM (PDT) @ Hall A
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
Francesco Locatello · Stefan Bauer · Mario Lucic · Gunnar Ratsch · Sylvain Gelly · Bernhard Schölkopf · Olivier Bachem
Break
Tue Jun 11 10:30 AM -- 11:00 AM (PDT)
Coffee Break
Oral
Tue Jun 11 11:00 AM -- 11:20 AM (PDT) @ Seaside Ballroom
Data Shapley: Equitable Valuation of Data for Machine Learning
In Supervised Learning
Amirata Ghorbani · James Zou
[ Video] 
Oral
Tue Jun 11 11:00 AM -- 11:20 AM (PDT) @ Grand Ballroom
Adversarial Attacks on Node Embeddings via Graph Poisoning
In Adversarial Examples
Aleksandar Bojchevski · Stephan Günnemann
[ Slides]  [ Video] 
Oral
Tue Jun 11 11:00 AM -- 11:20 AM (PDT) @ Room 101
A Contrastive Divergence for Combining Variational Inference and MCMC
In Approximate Inference
Francisco Ruiz · Michalis Titsias
[ Slides]  [ Video] 
Oral
Tue Jun 11 11:00 AM -- 11:20 AM (PDT) @ Hall B
ELF OpenGo: an analysis and open reimplementation of AlphaZero
In Deep RL 1
Yuandong Tian · Jerry Ma · Qucheng Gong · Shubho Sengupta · Zhuoyuan Chen · James Pinkerton · Larry Zitnick
[ Video] 
Oral
Tue Jun 11 11:00 AM -- 11:20 AM (PDT) @ Hall A
SelectiveNet: A Deep Neural Network with an Integrated Reject Option
In Deep Learning Algorithms
Yonatan Geifman · Ran El-Yaniv
[ Video] 
Oral
Tue Jun 11 11:00 AM -- 11:20 AM (PDT) @ Room 103
Refined Complexity of PCA with Outliers
In Learning Theory
Kirill Simonov · Fedor Fomin · Petr Golovach · Fahad Panolan
[ Video] 
Oral
Tue Jun 11 11:00 AM -- 11:20 AM (PDT) @ Room 102
Regret Circuits: Composability of Regret Minimizers
In Learning Theory: Games
Gabriele Farina · Christian Kroer · Tuomas Sandholm
[ Slides] 
Oral
Tue Jun 11 11:00 AM -- 11:20 AM (PDT) @ Room 201
Validating Causal Inference Models via Influence Functions
In Applications
Ahmed Alaa · Mihaela van der Schaar
[ Video] 
Oral
Tue Jun 11 11:00 AM -- 11:20 AM (PDT) @ Room 104
PA-GD: On the Convergence of Perturbed Alternating Gradient Descent to Second-Order Stationary Points for Structured Nonconvex Optimization
In Non-convex Optimization
Songtao Lu · Mingyi Hong · Zhengdao Wang
[ Slides]  [ Video] 
Oral
Tue Jun 11 11:20 AM -- 11:25 AM (PDT) @ Room 103
On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms
In Learning Theory
Tianyi Lin · Nhat Ho · Michael Jordan
[ Slides]  [ Video] 
Oral
Tue Jun 11 11:20 AM -- 11:25 AM (PDT) @ Room 101
Calibrated Approximate Bayesian Inference
In Approximate Inference
Hanwen Xing · Geoff Nicholls · Jeong Lee
[ Slides]  [ Video] 
Oral
Tue Jun 11 11:20 AM -- 11:25 AM (PDT) @ Room 104
Improved Zeroth-Order Variance Reduced Algorithms and Analysis for Nonconvex Optimization
In Non-convex Optimization
Kaiyi Ji · Zhe Wang · Yi Zhou · Yingbin LIANG
[ Slides]  [ Video] 
Oral
Tue Jun 11 11:20 AM -- 11:25 AM (PDT) @ Seaside Ballroom
Feature Grouping as a Stochastic Regularizer for High-Dimensional Structured Data
In Supervised Learning
Sergul Aydore · Thirion Bertrand · Gael Varoquaux
[ Slides]  [ Video] 
Oral
Tue Jun 11 11:20 AM -- 11:25 AM (PDT) @ Room 102
Game Theoretic Optimization via Gradient-based Nikaido-Isoda Function
In Learning Theory: Games
Arvind Raghunathan · Anoop Cherian · Devesh Jha
[ Slides] 
Oral
Tue Jun 11 11:20 AM -- 11:25 AM (PDT) @ Room 201
Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks
In Applications
Charith Mendis · Alex Renda · Dr.Saman Amarasinghe · Michael Carbin
[ Slides]  [ Video] 
Oral
Tue Jun 11 11:20 AM -- 11:25 AM (PDT) @ Hall B
Making Deep Q-learning methods robust to time discretization
In Deep RL 1
Corentin Tallec · Leonard Blier · Yann Ollivier
[ Slides]  [ Video] 
Oral
Tue Jun 11 11:20 AM -- 11:25 AM (PDT) @ Hall A
Manifold Mixup: Better Representations by Interpolating Hidden States
In Deep Learning Algorithms
Vikas Verma · Alex Lamb · Christopher Beckham · Amir Najafi · Ioannis Mitliagkas · David Lopez-Paz · Yoshua Bengio
[ Slides]  [ Video] 
Oral
Tue Jun 11 11:20 AM -- 11:25 AM (PDT) @ Grand Ballroom
First-Order Adversarial Vulnerability of Neural Networks and Input Dimension
In Adversarial Examples
Carl-Johann Simon-Gabriel · Yann Ollivier · Leon Bottou · Bernhard Schölkopf · David Lopez-Paz
[ Slides]  [ Video] 
Oral
Tue Jun 11 11:25 AM -- 11:30 AM (PDT) @ Hall A
Processing Megapixel Images with Deep Attention-Sampling Models
In Deep Learning Algorithms
Angelos Katharopoulos · Francois Fleuret
[ Slides]  [ Video] 
Oral
Tue Jun 11 11:25 AM -- 11:30 AM (PDT) @ Room 101
Moment-Based Variational Inference for Markov Jump Processes
In Approximate Inference
Christian Wildner · Heinz Koeppl
[ Slides]  [ Video] 
Oral
Tue Jun 11 11:25 AM -- 11:30 AM (PDT) @ Room 103
Passed & Spurious: Descent Algorithms and Local Minima in Spiked Matrix-Tensor Models
In Learning Theory
Stefano Sarao Mannelli · Florent Krzakala · Pierfrancesco Urbani · Lenka Zdeborova
[ Slides]  [ Video] 
Oral
Tue Jun 11 11:25 AM -- 11:30 AM (PDT) @ Room 201
Learning to Groove with Inverse Sequence Transformations
In Applications
Jon Gillick · Adam Roberts · Jesse Engel · Douglas Eck · David Bamman
[ Slides]  [ Video] 
Oral
Tue Jun 11 11:25 AM -- 11:30 AM (PDT) @ Room 102
Stable-Predictive Optimistic Counterfactual Regret Minimization
In Learning Theory: Games
Gabriele Farina · Christian Kroer · Noam Brown · Tuomas Sandholm
[ Slides] 
Oral
Tue Jun 11 11:25 AM -- 11:30 AM (PDT) @ Grand Ballroom
On Certifying Non-Uniform Bounds against Adversarial Attacks
In Adversarial Examples
Chen Liu · Ryota Tomioka · Volkan Cevher
[ Slides]  [ Video] 
Oral
Tue Jun 11 11:25 AM -- 11:30 AM (PDT) @ Seaside Ballroom
Metric-Optimized Example Weights
In Supervised Learning
Sen Zhao · Mahdi Milani Fard · Harikrishna Narasimhan · Maya Gupta
[ Slides]  [ Video] 
Oral
Tue Jun 11 11:25 AM -- 11:30 AM (PDT) @ Hall B
Nonlinear Distributional Gradient Temporal-Difference Learning
In Deep RL 1
chao qu · Shie Mannor · Huan Xu
[ Slides]  [ Video] 
Oral
Tue Jun 11 11:25 AM -- 11:30 AM (PDT) @ Room 104
Faster Stochastic Alternating Direction Method of Multipliers for Nonconvex Optimization
In Non-convex Optimization
Feihu Huang · Songcan Chen · Heng Huang
[ Slides]  [ Video] 
Oral
Tue Jun 11 11:30 AM -- 11:35 AM (PDT) @ Room 102
When Samples Are Strategically Selected
In Learning Theory: Games
Hanrui Zhang · Yu Cheng · Vincent Conitzer
[ Slides] 
Oral
Tue Jun 11 11:30 AM -- 11:35 AM (PDT) @ Hall B
Composing Entropic Policies using Divergence Correction
In Deep RL 1
Jonathan Hunt · Andre Barreto · Timothy Lillicrap · Nicolas Heess
[ Slides]  [ Video] 
Oral
Tue Jun 11 11:30 AM -- 11:35 AM (PDT) @ Seaside Ballroom
Improving Model Selection by Employing the Test Data
In Supervised Learning
Max Westphal · Werner Brannath
[ Slides]  [ Video] 
Oral
Tue Jun 11 11:30 AM -- 11:35 AM (PDT) @ Grand Ballroom
Improving Adversarial Robustness via Promoting Ensemble Diversity
In Adversarial Examples
Tianyu Pang · Kun Xu · Chao Du · Ning Chen · Jun Zhu
[ Slides]  [ Video] 
Oral
Tue Jun 11 11:30 AM -- 11:35 AM (PDT) @ Hall A
TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning
In Deep Learning Algorithms
Sung Whan Yoon · Jun Seo · Jaekyun Moon
[ Slides]  [ Video] 
Oral
Tue Jun 11 11:30 AM -- 11:35 AM (PDT) @ Room 104
Lower Bounds for Smooth Nonconvex Finite-Sum Optimization
In Non-convex Optimization
Dongruo Zhou · Quanquan Gu
[ Slides]  [ Video] 
Oral
Tue Jun 11 11:30 AM -- 11:35 AM (PDT) @ Room 201
Grid-Wise Control for Multi-Agent Reinforcement Learning in Video Game AI
In Applications
Lei Han · Peng Sun · Yali Du · Jiechao Xiong · Qing Wang · Xinghai Sun · Han Liu · Tong Zhang
[ Slides]  [ Video] 
Oral
Tue Jun 11 11:30 AM -- 11:35 AM (PDT) @ Room 103
Teaching a black-box learner
In Learning Theory
Sanjoy Dasgupta · Daniel Hsu · Stefanos Poulis · Jerry Zhu
[ Slides]  [ Video] 
Oral
Tue Jun 11 11:30 AM -- 11:35 AM (PDT) @ Room 101
Understanding MCMC Dynamics as Flows on the Wasserstein Space
In Approximate Inference
Chang Liu · Jingwei Zhuo · Jun Zhu
[ Slides]  [ Video] 
Oral
Tue Jun 11 11:35 AM -- 11:40 AM (PDT) @ Room 104
Nonconvex Variance Reduced Optimization with Arbitrary Sampling
In Non-convex Optimization
Samuel Horvath · Peter Richtarik
[ Slides]  [ Video] 
Oral
Tue Jun 11 11:35 AM -- 11:40 AM (PDT) @ Room 103
PAC Learnability of Node Functions in Networked Dynamical Systems
In Learning Theory
Abhijin Adiga · Chris J Kuhlman · Madhav Marathe · S. S. Ravi · Anil Vullikanti
[ Slides]  [ Video] 
Oral
Tue Jun 11 11:35 AM -- 11:40 AM (PDT) @ Seaside Ballroom
Topological Data Analysis of Decision Boundaries with Application to Model Selection
In Supervised Learning
Karthikeyan Ramamurthy · Kush Varshney · Krishnan Mody
[ Slides]  [ Video] 
Oral
Tue Jun 11 11:35 AM -- 11:40 AM (PDT) @ Room 101
LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations
In Approximate Inference
Brian Trippe · Jonathan Huggins · Raj Agrawal · Tamara Broderick
[ Slides]  [ Video] 
Oral
Tue Jun 11 11:35 AM -- 11:40 AM (PDT) @ Hall A
Online Meta-Learning
In Deep Learning Algorithms
Chelsea Finn · Aravind Rajeswaran · Sham Kakade · Sergey Levine
[ Slides]  [ Video] 
Oral
Tue Jun 11 11:35 AM -- 11:40 AM (PDT) @ Hall B
TibGM: A Transferable and Information-Based Graphical Model Approach for Reinforcement Learning
In Deep RL 1
Tameem Adel · Adrian Weller
[ Slides]  [ Video] 
Oral
Tue Jun 11 11:35 AM -- 11:40 AM (PDT) @ Room 102
Statistical Foundations of Virtual Democracy
In Learning Theory: Games
Anson Kahng · Min Kyung Lee · Ritesh Noothigattu · Ariel Procaccia · Christos-Alexandros Psomas
[ Slides] 
Oral
Tue Jun 11 11:35 AM -- 11:40 AM (PDT) @ Room 201
HOList: An Environment for Machine Learning of Higher Order Logic Theorem Proving
In Applications
Kshitij Bansal · Sarah Loos · Markus Rabe · Christian Szegedy · Stewart Wilcox
[ Slides]  [ Video] 
Oral
Tue Jun 11 11:35 AM -- 11:40 AM (PDT) @ Grand Ballroom
Adversarial camera stickers: A physical camera-based attack on deep learning systems
In Adversarial Examples
Juncheng Li · Frank R Schmidt · Zico Kolter
[ Slides]  [ Video] 
Oral
Tue Jun 11 11:40 AM -- 12:00 PM (PDT) @ Room 104
Error Feedback Fixes SignSGD and other Gradient Compression Schemes
In Non-convex Optimization
Sai Praneeth Reddy Karimireddy · Quentin Rebjock · Sebastian Stich · Martin Jaggi
[ Video] 
Oral
Tue Jun 11 11:40 AM -- 12:00 PM (PDT) @ Room 102
Optimal Auctions through Deep Learning
In Learning Theory: Games
Paul Duetting · Zhe Feng · Harikrishna Narasimhan · David Parkes · Sai Srivatsa Ravindranath
[ Slides] 
Oral
Tue Jun 11 11:40 AM -- 12:00 PM (PDT) @ Room 101
Amortized Monte Carlo Integration
In Approximate Inference
Adam Golinski · Frank Wood · Tom Rainforth
[ Slides]  [ Video] 
Oral
Tue Jun 11 11:40 AM -- 12:00 PM (PDT) @ Hall A
Training Neural Networks with Local Error Signals
In Deep Learning Algorithms
Arild Nøkland · Lars Hiller Eidnes
[ Slides]  [ Video] 
Oral
Tue Jun 11 11:40 AM -- 12:00 PM (PDT) @ Grand Ballroom
Adversarial examples from computational constraints
In Adversarial Examples
Sebastien Bubeck · Yin Tat Lee · Eric Price · Ilya Razenshteyn
[ Video] 
Oral
Tue Jun 11 11:40 AM -- 12:00 PM (PDT) @ Room 201
Molecular Hypergraph Grammar with Its Application to Molecular Optimization
In Applications
Hiroshi Kajino
[ Slides]  [ Video] 
Oral
Tue Jun 11 11:40 AM -- 12:00 PM (PDT) @ Hall B
Multi-Agent Adversarial Inverse Reinforcement Learning
In Deep RL 1
Lantao Yu · Jiaming Song · Stefano Ermon
[ Slides]  [ Video] 
Oral
Tue Jun 11 11:40 AM -- 12:00 PM (PDT) @ Seaside Ballroom
Contextual Memory Trees
In Supervised Learning
Wen Sun · Alina Beygelzimer · Hal Daumé III · John Langford · Paul Mineiro
[ Video] 
Oral
Tue Jun 11 11:40 AM -- 12:00 PM (PDT) @ Room 103
Online learning with kernel losses
In Learning Theory
Niladri Chatterji · Aldo Pacchiano · Peter Bartlett
[ Slides]  [ Video] 
Oral
Tue Jun 11 12:00 PM -- 12:05 PM (PDT) @ Room 103
Nearest Neighbor and Kernel Survival Analysis: Nonasymptotic Error Bounds and Strong Consistency Rates
In Learning Theory
George Chen
[ Slides]  [ Video] 
Oral
Tue Jun 11 12:00 PM -- 12:05 PM (PDT) @ Grand Ballroom
POPQORN: Quantifying Robustness of Recurrent Neural Networks
In Adversarial Examples
CHING-YUN KO · Zhaoyang Lyu · Tsui-Wei Weng · Luca Daniel · Ngai Wong · Dahua Lin
[ Slides]  [ Video] 
Oral
Tue Jun 11 12:00 PM -- 12:05 PM (PDT) @ Hall B
Policy Consolidation for Continual Reinforcement Learning
In Deep RL 1
Christos Kaplanis · Murray Shanahan · Claudia Clopath
[ Slides]  [ Video] 
Oral
Tue Jun 11 12:00 PM -- 12:05 PM (PDT) @ Room 101
Stein Point Markov Chain Monte Carlo
In Approximate Inference
Wilson Ye Chen · Alessandro Barp · Francois-Xavier Briol · Jackson Gorham · Mark Girolami · Lester Mackey · Chris Oates
[ Slides]  [ Video] 
Oral
Tue Jun 11 12:00 PM -- 12:05 PM (PDT) @ Seaside Ballroom
Sparse Extreme Multi-label Learning with Oracle Property
In Supervised Learning
Weiwei Liu · Xiaobo Shen
[ Slides]  [ Video] 
Oral
Tue Jun 11 12:00 PM -- 12:05 PM (PDT) @ Hall A
GMNN: Graph Markov Neural Networks
In Deep Learning Algorithms
Meng Qu · Yoshua Bengio · Jian Tang
[ Slides]  [ Video] 
Oral
Tue Jun 11 12:00 PM -- 12:05 PM (PDT) @ Room 104
A Composite Randomized Incremental Gradient Method
In Non-convex Optimization
Junyu Zhang · Lin Xiao
[ Slides]  [ Video] 
Oral
Tue Jun 11 12:00 PM -- 12:05 PM (PDT) @ Room 201
Graph Neural Network for Music Score Data and Modeling Expressive Piano Performance
In Applications
Dasaem Jeong · Taegyun Kwon · Yoojin Kim · Juhan Nam
[ Slides]  [ Video] 
Oral
Tue Jun 11 12:00 PM -- 12:05 PM (PDT) @ Room 102
Learning to Clear the Market
In Learning Theory: Games
Weiran Shen · Sébastien Lahaie · Renato Leme
[ Slides] 
Oral
Tue Jun 11 12:05 PM -- 12:10 PM (PDT) @ Room 102
Learning to bid in revenue-maximizing auctions
In Learning Theory: Games
Thomas Nedelec · Noureddine El Karoui · Vianney Perchet
[ Slides] 
Oral
Tue Jun 11 12:05 PM -- 12:10 PM (PDT) @ Room 201
Learning to Prove Theorems via Interacting with Proof Assistants
In Applications
Kaiyu Yang · Jia Deng
[ Slides]  [ Video] 
Oral
Tue Jun 11 12:05 PM -- 12:10 PM (PDT) @ Room 101
Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations
In Approximate Inference
Wu Lin · Mohammad Emtiyaz Khan · Mark Schmidt
[ Slides]  [ Video] 
Oral
Tue Jun 11 12:05 PM -- 12:10 PM (PDT) @ Hall B
Off-Policy Deep Reinforcement Learning without Exploration
In Deep RL 1
Scott Fujimoto · David Meger · Doina Precup
[ Slides]  [ Video] 
Oral
Tue Jun 11 12:05 PM -- 12:10 PM (PDT) @ Seaside Ballroom
Shape Constraints for Set Functions
In Supervised Learning
Andrew Cotter · Maya Gupta · Heinrich Jiang · Erez Louidor · James Muller · Taman Narayan · Serena Wang · Tao Zhu
[ Slides]  [ Video] 
Oral
Tue Jun 11 12:05 PM -- 12:10 PM (PDT) @ Room 104
Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field Inference
In Non-convex Optimization
Yatao Bian · Joachim Buhmann · Andreas Krause
[ Slides]  [ Video] 
Oral
Tue Jun 11 12:05 PM -- 12:10 PM (PDT) @ Grand Ballroom
Using Pre-Training Can Improve Model Robustness and Uncertainty
In Adversarial Examples
Dan Hendrycks · Kimin Lee · Mantas Mazeika
[ Slides]  [ Video] 
Oral
Tue Jun 11 12:05 PM -- 12:10 PM (PDT) @ Room 103
Fast Rates for a kNN Classifier Robust to Unknown Asymmetric Label Noise
In Learning Theory
Henry Reeve · Ata Kaban
[ Slides]  [ Video] 
Oral
Tue Jun 11 12:05 PM -- 12:10 PM (PDT) @ Hall A
Self-Attention Graph Pooling
In Deep Learning Algorithms
Junhyun Lee · Inyeop Lee · Jaewoo Kang
[ Slides]  [ Video] 
Oral
Tue Jun 11 12:10 PM -- 12:15 PM (PDT) @ Grand Ballroom
Generalized No Free Lunch Theorem for Adversarial Robustness
In Adversarial Examples
Elvis Dohmatob
[ Slides]  [ Video] 
Oral
Tue Jun 11 12:10 PM -- 12:15 PM (PDT) @ Room 101
Particle Flow Bayes' Rule
In Approximate Inference
Xinshi Chen · Hanjun Dai · Le Song
[ Slides]  [ Video] 
Oral
Tue Jun 11 12:10 PM -- 12:15 PM (PDT) @ Hall B
Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation
In Deep RL 1
Ruohan Wang · Carlo Ciliberto · Pierluigi Vito Amadori · Yiannis Demiris
[ Slides]  [ Video] 
Oral
Tue Jun 11 12:10 PM -- 12:15 PM (PDT) @ Room 104
Multiplicative Weights Updates as a distributed constrained optimization algorithm: Convergence to second-order stationary points almost always
In Non-convex Optimization
Ioannis Panageas · Georgios Piliouras · xiao wang
[ Slides]  [ Video] 
Oral
Tue Jun 11 12:10 PM -- 12:15 PM (PDT) @ Room 201
Circuit-GNN: Graph Neural Networks for Distributed Circuit Design
In Applications
GUO ZHANG · Hao He · Dina Katabi
[ Slides]  [ Video] 
Oral
Tue Jun 11 12:10 PM -- 12:15 PM (PDT) @ Room 103
Uniform Convergence Rate of the Kernel Density Estimator Adaptive to Intrinsic Volume Dimension
In Learning Theory
Jisu Kim · Jaehyeok Shin · Alessandro Rinaldo · Larry Wasserman
[ Slides]  [ Video] 
Oral
Tue Jun 11 12:10 PM -- 12:15 PM (PDT) @ Room 102
Open-ended learning in symmetric zero-sum games
In Learning Theory: Games
David Balduzzi · Marta Garnelo · Yoram Bachrach · Wojciech Czarnecki · Julien Perolat · Max Jaderberg · Thore Graepel
[ Slides] 
Oral
Tue Jun 11 12:10 PM -- 12:15 PM (PDT) @ Seaside Ballroom
On The Power of Curriculum Learning in Training Deep Networks
In Supervised Learning
Guy Hacohen · Daphna Weinshall
[ Slides]  [ Video] 
Oral
Tue Jun 11 12:10 PM -- 12:15 PM (PDT) @ Hall A
Combating Label Noise in Deep Learning using Abstention
In Deep Learning Algorithms
Sunil Thulasidasan · Tanmoy Bhattacharya · Jeff Bilmes · Gopinath Chennupati · Jamal Mohd-Yusof
[ Slides]  [ Video] 
Oral
Tue Jun 11 12:15 PM -- 12:20 PM (PDT) @ Room 101
Correlated Variational Auto-Encoders
In Approximate Inference
Da Tang · Dawen Liang · Tony Jebara · Nicholas Ruozzi
[ Slides]  [ Video] 
Oral
Tue Jun 11 12:15 PM -- 12:20 PM (PDT) @ Room 201
Learning to Optimize Multigrid PDE Solvers
In Applications
Daniel Greenfeld · Meirav Galun · Ronen Basri · Irad Yavneh · Ron Kimmel
[ Slides]  [ Video] 
Oral
Tue Jun 11 12:15 PM -- 12:20 PM (PDT) @ Hall A
LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning
In Deep Learning Algorithms
Huaiyu Li · Weiming Dong · Xing Mei · Chongyang Ma · Feiyue Huang · Bao-Gang Hu
[ Slides]  [ Video] 
Oral
Tue Jun 11 12:15 PM -- 12:20 PM (PDT) @ Room 102
Deep Counterfactual Regret Minimization
In Learning Theory: Games
Noam Brown · Adam Lerer · Sam Gross · Tuomas Sandholm
[ Slides] 
Oral
Tue Jun 11 12:15 PM -- 12:20 PM (PDT) @ Hall B
Revisiting the Softmax Bellman Operator: New Benefits and New Perspective
In Deep RL 1
Zhao Song · Ron Parr · Lawrence Carin
[ Slides]  [ Video] 
Oral
Tue Jun 11 12:15 PM -- 12:20 PM (PDT) @ Room 103
Maximum Likelihood Estimation for Learning Populations of Parameters
In Learning Theory
Ramya Korlakai Vinayak · Weihao Kong · Gregory Valiant · Sham Kakade
[ Slides]  [ Video] 
Oral
Tue Jun 11 12:15 PM -- 12:20 PM (PDT) @ Seaside Ballroom
Voronoi Boundary Classification: A High-Dimensional Geometric Approach via Weighted Monte Carlo Integration
In Supervised Learning
Vladislav Polianskii · Florian T. Pokorny
[ Slides]  [ Video] 
Oral
Tue Jun 11 12:15 PM -- 12:20 PM (PDT) @ Grand Ballroom
PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach
In Adversarial Examples
Tsui-Wei Weng · Pin-Yu Chen · Lam Nguyen · Mark Squillante · Akhilan Boopathy · Ivan Oseledets · Luca Daniel
[ Slides]  [ Video] 
Oral
Tue Jun 11 12:15 PM -- 12:20 PM (PDT) @ Room 104
Katalyst: Boosting Convex Katayusha for Non-Convex Problems with a Large Condition Number
In Non-convex Optimization
Zaiyi Chen · Yi Xu · Haoyuan Hu · Tianbao Yang
[ Slides]  [ Video] 
Break
Tue Jun 11 12:30 PM -- 02:00 PM (PDT)
Lunch - on your own
Oral
Tue Jun 11 02:00 PM -- 02:20 PM (PDT) @ Grand Ballroom
On Learning Invariant Representations for Domain Adaptation
In Deep Learning Theory
Han Zhao · Remi Tachet des Combes · Kun Zhang · Geoff Gordon
[ Video] 
Oral
Tue Jun 11 02:00 PM -- 02:20 PM (PDT) @ Room 102
Generalized Approximate Survey Propagation for High-Dimensional Estimation
In Information Theory and Estimation
Carlo Lucibello · Luca Saglietti · Yue Lu
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:00 PM -- 02:20 PM (PDT) @ Room 201
A Block Coordinate Descent Proximal Method for Simultaneous Filtering and Parameter Estimation
In Time Series
Ramin Raziperchikolaei · Harish Bhat
[ Video] 
Oral
Tue Jun 11 02:00 PM -- 02:20 PM (PDT) @ Room 101
Towards a Unified Analysis of Random Fourier Features
In Kernel Methods
Zhu Li · Jean-Francois Ton · Dino Oglic · Dino Sejdinovic
[ Video] 
Oral
Tue Jun 11 02:00 PM -- 02:20 PM (PDT) @ Room 103
Projection onto Minkowski Sums with Application to Constrained Learning
In Convex Optimization
Joong-Ho (Johann) Won · Jason Xu · Kenneth Lange
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:00 PM -- 02:20 PM (PDT) @ Seaside Ballroom
Robust Decision Trees Against Adversarial Examples
In Supervised Learning
Hongge Chen · Huan Zhang · Duane Boning · Cho-Jui Hsieh
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:00 PM -- 02:20 PM (PDT) @ Room 104
Safe Policy Improvement with Baseline Bootstrapping
In Reinforcement Learning Theory
Romain Laroche · Paul TRICHELAIR · Remi Tachet des Combes
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:00 PM -- 02:20 PM (PDT) @ Hall B
An Investigation of Model-Free Planning
In Deep RL 2
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
[ Video] 
Oral
Tue Jun 11 02:00 PM -- 02:20 PM (PDT) @ Hall A
Self-Attention Generative Adversarial Networks
In Generative Adversarial Networks
Han Zhang · Ian Goodfellow · Dimitris Metaxas · Augustus Odena
[ Video] 
Oral
Tue Jun 11 02:20 PM -- 02:25 PM (PDT) @ Room 201
Learning Hawkes Processes Under Synchronization Noise
In Time Series
William Trouleau · Jalal Etesami · Matthias Grossglauser · Negar Kiyavash · Patrick Thiran
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:20 PM -- 02:25 PM (PDT) @ Seaside Ballroom
Automatic Classifiers as Scientific Instruments: One Step Further Away from Ground-Truth
In Supervised Learning
Jacob Whitehill · Anand Ramakrishnan
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:20 PM -- 02:25 PM (PDT) @ Room 102
Boosted Density Estimation Remastered
In Information Theory and Estimation
Zac Cranko · Richard Nock
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:20 PM -- 02:25 PM (PDT) @ Hall B
CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning
In Deep RL 2
Cédric Colas · Pierre-Yves Oudeyer · Olivier Sigaud · Pierre Fournier · Mohamed Chetouani
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:20 PM -- 02:25 PM (PDT) @ Hall A
Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution Matching
In Generative Adversarial Networks
Ziliang Chen · ZHANFU YANG · Xiaoxi Wang · Xiaodan Liang · xiaopeng yan · Guanbin Li · Liang Lin
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:20 PM -- 02:25 PM (PDT) @ Room 103
Blended Conditonal Gradients
In Convex Optimization
Gábor Braun · Sebastian Pokutta · Dan Tu · Stephen Wright
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:20 PM -- 02:25 PM (PDT) @ Room 104
Distributional Reinforcement Learning for Efficient Exploration
In Reinforcement Learning Theory
Borislav Mavrin · Hengshuai Yao · Linglong Kong · Kaiwen Wu · Yaoliang Yu
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:20 PM -- 02:25 PM (PDT) @ Grand Ballroom
Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models
In Deep Learning Theory
Mor Shpigel Nacson · Suriya Gunasekar · Jason Lee · Nati Srebro · Daniel Soudry
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:20 PM -- 02:25 PM (PDT) @ Room 101
Learning deep kernels for exponential family densities
In Kernel Methods
Li Kevin Wenliang · D.J. Sutherland · Heiko Strathmann · Arthur Gretton
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:25 PM -- 02:30 PM (PDT) @ Room 103
Acceleration of SVRG and Katyusha X by Inexact Preconditioning
In Convex Optimization
Yanli Liu · Fei Feng · Wotao Yin
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:25 PM -- 02:30 PM (PDT) @ Room 104
Optimistic Policy Optimization via Multiple Importance Sampling
In Reinforcement Learning Theory
Matteo Papini · Alberto Maria Metelli · Lorenzo Lupo · Marcello Restelli
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:25 PM -- 02:30 PM (PDT) @ Hall B
Task-Agnostic Dynamics Priors for Deep Reinforcement Learning
In Deep RL 2
Yilun Du · Karthik Narasimhan
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:25 PM -- 02:30 PM (PDT) @ Hall A
High-Fidelity Image Generation With Fewer Labels
In Generative Adversarial Networks
Mario Lucic · Michael Tschannen · Marvin Ritter · Xiaohua Zhai · Olivier Bachem · Sylvain Gelly
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:25 PM -- 02:30 PM (PDT) @ Room 201
Generative Adversarial User Model for Reinforcement Learning Based Recommendation System
In Time Series
Xinshi Chen · Shuang Li · Hui Li · Shaohua Jiang · Yuan Qi · Le Song
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:25 PM -- 02:30 PM (PDT) @ Room 102
Inference and Sampling of $K_{33}$-free Ising Models
In Information Theory and Estimation
Valerii Likhosherstov · Yury Maximov · Misha Chertkov
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:25 PM -- 02:30 PM (PDT) @ Grand Ballroom
Adversarial Generation of Time-Frequency Features with application in audio synthesis
In Deep Learning Theory
Andrés Marafioti · Nathanaël Perraudin · Nicki Holighaus · Piotr Majdak
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:25 PM -- 02:30 PM (PDT) @ Seaside Ballroom
Look Ma, No Latent Variables: Accurate Cutset Networks via Compilation
In Supervised Learning
Tahrima Rahman · Shasha Jin · Vibhav Gogate
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:25 PM -- 02:30 PM (PDT) @ Room 101
Bayesian Deconditional Kernel Mean Embeddings
In Kernel Methods
Kelvin Hsu · Fabio Ramos
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:30 PM -- 02:35 PM (PDT) @ Seaside Ballroom
Optimal Transport for structured data with application on graphs
In Supervised Learning
Titouan Vayer · Nicolas Courty · Romain Tavenard · Chapel Laetitia · Remi Flamary
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:30 PM -- 02:35 PM (PDT) @ Room 101
A Kernel Perspective for Regularizing Deep Neural Networks
In Kernel Methods
Alberto Bietti · Gregoire Mialon · Dexiong Chen · Julien Mairal
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:30 PM -- 02:35 PM (PDT) @ Room 102
Random Matrix Improved Covariance Estimation for a Large Class of Metrics
In Information Theory and Estimation
Malik TIOMOKO A · Romain Couillet · Florent BOUCHARD · Guillaume GINOLHAC
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:30 PM -- 02:35 PM (PDT) @ Hall A
Revisiting precision recall definition for generative modeling
In Generative Adversarial Networks
Loic Simon · Ryan Webster · Julien Rabin
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:30 PM -- 02:35 PM (PDT) @ Room 201
A Statistical Investigation of Long Memory in Language and Music
In Time Series
Alexander Greaves-Tunnell · Zaid Harchaoui
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:30 PM -- 02:35 PM (PDT) @ Hall B
Diagnosing Bottlenecks in Deep Q-learning Algorithms
In Deep RL 2
Justin Fu · Aviral Kumar · Matthew Soh · Sergey Levine
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:30 PM -- 02:35 PM (PDT) @ Grand Ballroom
On the Universality of Invariant Networks
In Deep Learning Theory
Haggai Maron · Ethan Fetaya · Nimrod Segol · Yaron Lipman
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:30 PM -- 02:35 PM (PDT) @ Room 103
Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD
In Convex Optimization
Marten van Dijk · Lam Nguyen · PHUONG_HA NGUYEN · Dzung Phan
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:30 PM -- 02:35 PM (PDT) @ Room 104
Neural Logic Reinforcement Learning
In Reinforcement Learning Theory
zhengyao jiang · Shan Luo
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:35 PM -- 02:40 PM (PDT) @ Room 101
A Persistent Weisfeiler--Lehman Procedure for Graph Classification
In Kernel Methods
Bastian Rieck · Christian Bock · Karsten Borgwardt
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:35 PM -- 02:40 PM (PDT) @ Seaside Ballroom
Learning Optimal Linear Regularizers
In Supervised Learning
Matthew Streeter
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:35 PM -- 02:40 PM (PDT) @ Room 201
Deep Factors for Forecasting
In Time Series
Yuyang Wang · Alex Smola · Danielle Robinson · Jan Gasthaus · Dean Foster · Tim Januschowski
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:35 PM -- 02:40 PM (PDT) @ Room 103
A Conditional-Gradient-Based Augmented Lagrangian Framework
In Convex Optimization
Alp Yurtsever · Olivier Fercoq · Volkan Cevher
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:35 PM -- 02:40 PM (PDT) @ Hall A
Wasserstein of Wasserstein Loss for Learning Generative Models
In Generative Adversarial Networks
Yonatan Dukler · Wuchen Li · Alex Lin · Guido Montufar
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:35 PM -- 02:40 PM (PDT) @ Room 104
Learning to Collaborate in Markov Decision Processes
In Reinforcement Learning Theory
Goran Radanovic · Rati Devidze · David Parkes · Adish Singla
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:35 PM -- 02:40 PM (PDT) @ Grand Ballroom
Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks
In Deep Learning Theory
Sanjeev Arora · Simon Du · Wei Hu · Zhiyuan Li · Ruosong Wang
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:35 PM -- 02:40 PM (PDT) @ Hall B
Collaborative Evolutionary Reinforcement Learning
In Deep RL 2
Shauharda Khadka · Somdeb Majumdar · Tarek Nassar · Zach Dwiel · Evren Tumer · Santiago Miret · Yinyin Liu · Kagan Tumer
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:35 PM -- 02:40 PM (PDT) @ Room 102
Dual Entangled Polynomial Code: Three-Dimensional Coding for Distributed Matrix Multiplication
In Information Theory and Estimation
Pedro Soto · Jun Li · Xiaodi Fan
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:40 PM -- 03:00 PM (PDT) @ Room 104
Predictor-Corrector Policy Optimization
In Reinforcement Learning Theory
Ching-An Cheng · Xinyan Yan · Nathan Ratliff · Byron Boots
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:40 PM -- 03:00 PM (PDT) @ Hall A
Flat Metric Minimization with Applications in Generative Modeling
In Generative Adversarial Networks
Thomas Möllenhoff · Daniel Cremers
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:40 PM -- 03:00 PM (PDT) @ Hall B
EMI: Exploration with Mutual Information
In Deep RL 2
Hyoungseok Kim · Jaekyeom Kim · Yeonwoo Jeong · Sergey Levine · Hyun Oh Song
[ Video] 
Oral
Tue Jun 11 02:40 PM -- 03:00 PM (PDT) @ Room 201
Weakly-Supervised Temporal Localization via Occurrence Count Learning
In Time Series
Julien Schroeter · Kirill Sidorov · David Marshall
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:40 PM -- 03:00 PM (PDT) @ Grand Ballroom
Gauge Equivariant Convolutional Networks and the Icosahedral CNN
In Deep Learning Theory
Taco Cohen · Maurice Weiler · Berkay Kicanaoglu · Max Welling
[ Video] 
Oral
Tue Jun 11 02:40 PM -- 03:00 PM (PDT) @ Seaside Ballroom
On Symmetric Losses for Learning from Corrupted Labels
In Supervised Learning
Nontawat Charoenphakdee · Jongyeong Lee · Masashi Sugiyama
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:40 PM -- 03:00 PM (PDT) @ Room 102
Neural Joint Source-Channel Coding
In Information Theory and Estimation
Kristy Choi · Kedar Tatwawadi · Aditya Grover · Tsachy Weissman · Stefano Ermon
[ Slides]  [ Video] 
Oral
Tue Jun 11 02:40 PM -- 03:00 PM (PDT) @ Room 103
SGD: General Analysis and Improved Rates
In Convex Optimization
Robert Gower · Nicolas Loizou · Xun Qian · Alibek Sailanbayev · Egor Shulgin · Peter Richtarik
[ Video] 
Oral
Tue Jun 11 02:40 PM -- 03:00 PM (PDT) @ Room 101
Rehashing Kernel Evaluation in High Dimensions
In Kernel Methods
Paris Siminelakis · Kexin Rong · Peter Bailis · Moses Charikar · Philip Levis
[ Slides]  [ Video] 
Oral
Tue Jun 11 03:00 PM -- 03:05 PM (PDT) @ Room 102
Doubly-Competitive Distribution Estimation
In Information Theory and Estimation
Yi Hao · Alon Orlitsky
[ Slides]  [ Video] 
Oral
Tue Jun 11 03:00 PM -- 03:05 PM (PDT) @ Hall B
Imitation Learning from Imperfect Demonstration
In Deep RL 2
Yueh-Hua Wu · Nontawat Charoenphakdee · Han Bao · Voot Tangkaratt · Masashi Sugiyama
[ Slides]  [ Video] 
Oral
Tue Jun 11 03:00 PM -- 03:05 PM (PDT) @ Room 201
Switching Linear Dynamics for Variational Bayes Filtering
In Time Series
Philip Becker-Ehmck · Jan Peters · Patrick van der Smagt
[ Slides]  [ Video] 
Oral
Tue Jun 11 03:00 PM -- 03:05 PM (PDT) @ Room 101
Large-Scale Sparse Kernel Canonical Correlation Analysis
In Kernel Methods
Viivi Uurtio · Sahely Bhadra · Juho Rousu
Oral
Tue Jun 11 03:00 PM -- 03:05 PM (PDT) @ Room 103
Curvature-Exploiting Acceleration of Elastic Net Computations
In Convex Optimization
Vien Mai · Mikael Johansson
[ Slides]  [ Video] 
Oral
Tue Jun 11 03:00 PM -- 03:05 PM (PDT) @ Hall A
Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs
In Generative Adversarial Networks
Yogesh Balaji · Hamed Hassani · Rama Chellappa · Soheil Feizi
[ Slides]  [ Video] 
Oral
Tue Jun 11 03:00 PM -- 03:05 PM (PDT) @ Seaside Ballroom
AUCµ: A Performance Metric for Multi-Class Machine Learning Models
In Supervised Learning
Ross Kleiman · University of Wisconsin David Page
[ Slides] 
Oral
Tue Jun 11 03:00 PM -- 03:05 PM (PDT) @ Room 104
Learning a Prior over Intent via Meta-Inverse Reinforcement Learning
In Reinforcement Learning Theory
Kelvin Xu · Ellis Ratner · Anca Dragan · Sergey Levine · Chelsea Finn
[ Slides]  [ Video] 
Oral
Tue Jun 11 03:00 PM -- 03:05 PM (PDT) @ Grand Ballroom
Feature-Critic Networks for Heterogeneous Domain Generalization
In Deep Learning Theory
Yiying Li · Yongxin Yang · Wei Zhou · Timothy Hospedales
[ Slides]  [ Video] 
Oral
Tue Jun 11 03:05 PM -- 03:10 PM (PDT) @ Grand Ballroom
Learning to Convolve: A Generalized Weight-Tying Approach
In Deep Learning Theory
Nichita Diaconu · Daniel E Worrall
[ Slides]  [ Video] 
Oral
Tue Jun 11 03:05 PM -- 03:10 PM (PDT) @ Room 103
Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication
In Convex Optimization
Anastasiia Koloskova · Sebastian Stich · Martin Jaggi
[ Slides]  [ Video] 
Oral
Tue Jun 11 03:05 PM -- 03:10 PM (PDT) @ Room 104
DeepMDP: Learning Continuous Latent Space Models for Representation Learning
In Reinforcement Learning Theory
Carles Gelada · Saurabh Kumar · Jacob Buckman · Ofir Nachum · Marc Bellemare
[ Slides]  [ Video] 
Oral
Tue Jun 11 03:05 PM -- 03:10 PM (PDT) @ Room 201
Imputing Missing Events in Continuous-Time Event Streams
In Time Series
Hongyuan Mei · Guanghui Qin · Jason Eisner
[ Slides]  [ Video] 
Oral
Tue Jun 11 03:05 PM -- 03:10 PM (PDT) @ Room 102
Homomorphic Sensing
In Information Theory and Estimation
Manolis Tsakiris · Liangzu Peng
[ Slides]  [ Video] 
Oral
Tue Jun 11 03:05 PM -- 03:10 PM (PDT) @ Hall A
Non-Parametric Priors For Generative Adversarial Networks
In Generative Adversarial Networks
Rajhans Singh · Pavan Turaga · Suren Jayasuriya · Ravi Garg · Martin Braun
[ Slides]  [ Video] 
Oral
Tue Jun 11 03:05 PM -- 03:10 PM (PDT) @ Room 101
A Kernel Theory of Modern Data Augmentation
In Kernel Methods
Tri Dao · Albert Gu · Alexander J Ratner · Virginia Smith · Christopher De Sa · Christopher Re
[ Slides]  [ Video] 
Oral
Tue Jun 11 03:05 PM -- 03:10 PM (PDT) @ Hall B
Curiosity-Bottleneck: Exploration By Distilling Task-Specific Novelty
In Deep RL 2
Youngjin Kim · Daniel Nam · Hyunwoo Kim · Ji-Hoon Kim · Gunhee Kim
[ Slides]  [ Video] 
Oral
Tue Jun 11 03:05 PM -- 03:10 PM (PDT) @ Seaside Ballroom
Regularization in directable environments with application to Tetris
In Supervised Learning
Jan Malte Lichtenberg · Ozgur Simsek
[ Slides]  [ Video] 
Oral
Tue Jun 11 03:10 PM -- 03:15 PM (PDT) @ Hall B
Dynamic Weights in Multi-Objective Deep Reinforcement Learning
In Deep RL 2
Axel Abels · Diederik Roijers · Tom Lenaerts · Ann Nowé · Denis Steckelmacher
[ Slides]  [ Video] 
Oral
Tue Jun 11 03:10 PM -- 03:15 PM (PDT) @ Room 103
Safe Grid Search with Optimal Complexity
In Convex Optimization
Eugene Ndiaye · Tam Le · Olivier Fercoq · Joseph Salmon · Ichiro Takeuchi
[ Slides]  [ Video] 
Oral
Tue Jun 11 03:10 PM -- 03:15 PM (PDT) @ Seaside Ballroom
Improved Dynamic Graph Learning through Fault-Tolerant Sparsification
In Supervised Learning
Chunjiang Zhu · Sabine Storandt · Kam-Yiu Lam · Song Han · Jinbo Bi
[ Slides]  [ Video] 
Oral
Tue Jun 11 03:10 PM -- 03:15 PM (PDT) @ Room 104
Importance Sampling Policy Evaluation with an Estimated Behavior Policy
In Reinforcement Learning Theory
Josiah Hanna · Scott Niekum · Peter Stone
[ Slides]  [ Video] 
Oral
Tue Jun 11 03:10 PM -- 03:15 PM (PDT) @ Room 201
Understanding and Controlling Memory in Recurrent Neural Networks
In Time Series
Doron Haviv · Alexander Rivkind · Omri Barak
[ Slides]  [ Video] 
Oral
Tue Jun 11 03:10 PM -- 03:15 PM (PDT) @ Hall A
Lipschitz Generative Adversarial Nets
In Generative Adversarial Networks
Zhiming Zhou · Jiadong Liang · Yuxuan Song · Lantao Yu · Hongwei Wang · Weinan Zhang · Yong Yu · Zhihua Zhang
[ Slides]  [ Video] 
Oral
Tue Jun 11 03:10 PM -- 03:15 PM (PDT) @ Grand Ballroom
On Dropout and Nuclear Norm Regularization
In Deep Learning Theory
Poorya Mianjy · Raman Arora
[ Slides]  [ Video] 
Oral
Tue Jun 11 03:10 PM -- 03:15 PM (PDT) @ Room 101
kernelPSI: a Post-Selection Inference Framework for Nonlinear Variable Selection
In Kernel Methods
Lotfi Slim · Clément Chatelain · Chloe-Agathe Azencott · Jean-Philippe Vert
[ Slides]  [ Video] 
Oral
Tue Jun 11 03:10 PM -- 03:15 PM (PDT) @ Room 102
Phaseless PCA: Low-Rank Matrix Recovery from Column-wise Phaseless Measurements
In Information Theory and Estimation
Seyedehsara Nayer · Praneeth Narayanamurthy · Namrata Vaswani
[ Slides]  [ Video] 
Oral
Tue Jun 11 03:15 PM -- 03:20 PM (PDT) @ Grand Ballroom
Gradient Descent Finds Global Minima of Deep Neural Networks
In Deep Learning Theory
Simon Du · Jason Lee · Haochuan Li · Liwei Wang · Xiyu Zhai
[ Slides]  [ Video] 
Oral
Tue Jun 11 03:15 PM -- 03:20 PM (PDT) @ Hall B
Fingerprint Policy Optimisation for Robust Reinforcement Learning
In Deep RL 2
Supratik Paul · Michael A Osborne · Shimon Whiteson
[ Slides]  [ Video] 
Oral
Tue Jun 11 03:15 PM -- 03:20 PM (PDT) @ Hall A
HexaGAN: Generative Adversarial Nets for Real World Classification
In Generative Adversarial Networks
Uiwon Hwang · Dahuin Jung · Sungroh Yoon
[ Slides]  [ Video] 
Oral
Tue Jun 11 03:15 PM -- 03:20 PM (PDT) @ Room 102
Rate Distortion For Model Compression:From Theory To Practice
In Information Theory and Estimation
Weihao Gao · Yu-Han Liu · Chong Wang · Sewoong Oh
[ Slides]  [ Video] 
Oral
Tue Jun 11 03:15 PM -- 03:20 PM (PDT) @ Room 101
Scalable Learning in Reproducing Kernel Krein Spaces
In Kernel Methods
Dino Oglic · Thomas Gaertner
[ Slides]  [ Video] 
Oral
Tue Jun 11 03:15 PM -- 03:20 PM (PDT) @ Room 103
SAGA with Arbitrary Sampling
In Convex Optimization
Xun Qian · Zheng Qu · Peter Richtarik
[ Slides]  [ Video] 
Oral
Tue Jun 11 03:15 PM -- 03:20 PM (PDT) @ Seaside Ballroom
Heterogeneous Model Reuse via Optimizing Multiparty Multiclass Margin
In Supervised Learning
Xi-Zhu Wu · Song Liu · Zhi-Hua Zhou
[ Slides]  [ Video] 
Oral
Tue Jun 11 03:15 PM -- 03:20 PM (PDT) @ Room 104
Learning from a Learner
In Reinforcement Learning Theory
alexis jacq · Matthieu Geist · Ana Paiva · Olivier Pietquin
[ Slides]  [ Video] 
Oral
Tue Jun 11 03:15 PM -- 03:20 PM (PDT) @ Room 201
Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces
In Time Series
Philipp Becker · Harit Pandya · Gregor Gebhardt · Cheng Zhao · C. James Taylor · Gerhard Neumann
[ Slides]  [ Video] 
Break
Tue Jun 11 03:30 PM -- 04:00 PM (PDT)
Coffee break
Oral
Tue Jun 11 04:00 PM -- 04:20 PM (PDT) @ Hall A
Graph Matching Networks for Learning the Similarity of Graph Structured Objects
In Deep Learning Architectures
Yujia Li · Chenjie Gu · Thomas Dullien · Oriol Vinyals · Pushmeet Kohli
[ Slides]  [ Video] 
Oral
Tue Jun 11 04:00 PM -- 04:20 PM (PDT) @ Seaside Ballroom
Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff
In Applications: Computer Vision
Yochai Blau · Tomer Michaeli
[ Video] 
Oral
Tue Jun 11 04:00 PM -- 04:20 PM (PDT) @ Hall B
An Investigation into Neural Net Optimization via Hessian Eigenvalue Density
In Deep Learning Optimization
Behrooz Ghorbani · Shankar Krishnan · Ying Xiao
[ Slides]  [ Video] 
Oral
Tue Jun 11 04:00 PM -- 04:20 PM (PDT) @ Room 101
Dirichlet Simplex Nest and Geometric Inference
In Bayesian Methods
Mikhail Yurochkin · Aritra Guha · Yuekai Sun · XuanLong Nguyen
[ Slides]  [ Video] 
Oral
Tue Jun 11 04:00 PM -- 04:20 PM (PDT) @ Room 104
Separable value functions across time-scales
In Reinforcement Learning Theory
Joshua Romoff · Peter Henderson · Ahmed Touati · Yann Ollivier · Joelle Pineau · Emma Brunskill
[ Video] 
Oral
Tue Jun 11 04:00 PM -- 04:20 PM (PDT) @ Room 201
Subspace Robust Wasserstein Distances
In General ML
François-Pierre Paty · Marco Cuturi
[ Video] 
Oral
Tue Jun 11 04:00 PM -- 04:20 PM (PDT) @ Room 102
Formal Privacy for Functional Data with Gaussian Perturbations
In Privacy
Ardalan Mirshani · Matthew Reimherr · Aleksandra Slavković
[ Slides]  [ Video] 
Oral
Tue Jun 11 04:00 PM -- 04:20 PM (PDT) @ Grand Ballroom
Composable Core-sets for Determinant Maximization: A Simple Near-Optimal Algorithm
In Large Scale Learning and Systems
Sepideh Mahabadi · Piotr Indyk · Shayan Oveis Gharan · Alireza Rezaei
[ Slides]  [ Video] 
Oral
Tue Jun 11 04:00 PM -- 04:20 PM (PDT) @ Room 103
Natural Analysts in Adaptive Data Analysis
In Statistical Learning Theory
Tijana Zrnic · Moritz Hardt
[ Slides]  [ Video] 
Oral
Tue Jun 11 04:20 PM -- 04:25 PM (PDT) @ Room 102
Graphical-model based estimation and inference for differential privacy
In Privacy
Ryan McKenna · Daniel Sheldon · Gerome Miklau
[ Slides]  [ Video] 
Oral
Tue Jun 11 04:20 PM -- 04:25 PM (PDT) @ Grand Ballroom
Sublinear Time Nearest Neighbor Search over Generalized Weighted Space
In Large Scale Learning and Systems
Yifan Lei · Qiang Huang · Mohan Kankanhalli · Anthony Tung
[ Slides]  [ Video] 
Oral
Tue Jun 11 04:20 PM -- 04:25 PM (PDT) @ Hall A
BayesNAS: A Bayesian Approach for Neural Architecture Search
In Deep Learning Architectures
Hongpeng Zhou · Minghao Yang · Jun Wang · Wei Pan
[ Slides]  [ Video] 
Oral
Tue Jun 11 04:20 PM -- 04:25 PM (PDT) @ Room 201
Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models
In General ML
Kaspar Märtens · Kieran Campbell · Christopher Yau
[ Slides]  [ Video] 
Oral
Tue Jun 11 04:20 PM -- 04:25 PM (PDT) @ Seaside Ballroom
Collaborative Channel Pruning for Deep Networks
In Applications: Computer Vision
Hanyu Peng · Jiaxiang Wu · Shifeng Chen · Junzhou Huang
[ Slides]  [ Video] 
Oral
Tue Jun 11 04:20 PM -- 04:25 PM (PDT) @ Room 101
Bayesian leave-one-out cross-validation for large data
In Bayesian Methods
Måns Magnusson · Michael Andersen · Johan Jonasson · Aki Vehtari
[ Slides]  [ Video] 
Oral
Tue Jun 11 04:20 PM -- 04:25 PM (PDT) @ Hall B
Differentiable Linearized ADMM
In Deep Learning Optimization
Xingyu Xie · Jianlong Wu · Guangcan Liu · Zhisheng Zhong · Zhouchen Lin
Oral
Tue Jun 11 04:20 PM -- 04:25 PM (PDT) @ Room 103
CapsAndRuns: An Improved Method for Approximately Optimal Algorithm Configuration
In Statistical Learning Theory
Gellért Weisz · András György · Csaba Szepesvari
[ Slides]  [ Video] 
Oral
Tue Jun 11 04:20 PM -- 04:25 PM (PDT) @ Room 104
Learning Action Representations for Reinforcement Learning
In Reinforcement Learning Theory
Yash Chandak · Georgios Theocharous · James Kostas · Scott Jordan · Philip Thomas
[ Slides]  [ Video] 
Oral
Tue Jun 11 04:25 PM -- 04:30 PM (PDT) @ Room 102
White-box vs Black-box: Bayes Optimal Strategies for Membership Inference
In Privacy
Alexandre Sablayrolles · Douze Matthijs · Cordelia Schmid · Yann Ollivier · Herve Jegou
[ Slides]  [ Video] 
Oral
Tue Jun 11 04:25 PM -- 04:30 PM (PDT) @ Hall A
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks
In Deep Learning Architectures
Juho Lee · Yoonho Lee · Jungtaek Kim · Adam Kosiorek · Seungjin Choi · Yee-Whye Teh
[ Slides]  [ Video] 
Oral
Tue Jun 11 04:25 PM -- 04:30 PM (PDT) @ Grand Ballroom
Compressing Gradient Optimizers via Count-Sketches
In Large Scale Learning and Systems
Ryan Spring · Anastasios Kyrillidis · Vijai Mohan · Anshumali Shrivastava
[ Slides]  [ Video] 
Oral
Tue Jun 11 04:25 PM -- 04:30 PM (PDT) @ Room 101
Rao-Blackwellized Stochastic Gradients for Discrete Distributions
In Bayesian Methods
Runjing Liu · Jeffrey Regier · Nilesh Tripuraneni · Michael Jordan · Jon McAuliffe
[ Slides]  [ Video] 
Oral
Tue Jun 11 04:25 PM -- 04:30 PM (PDT) @ Room 104
Bayesian Counterfactual Risk Minimization
In Reinforcement Learning Theory
Ben London · Ted Sandler
[ Slides]  [ Video] 
Oral
Tue Jun 11 04:25 PM -- 04:30 PM (PDT) @ Room 103
Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction
In Statistical Learning Theory
Giulia Luise · Dimitrios Stamos · Massimiliano Pontil · Carlo Ciliberto
[ Slides]  [ Video] 
Oral
Tue Jun 11 04:25 PM -- 04:30 PM (PDT) @ Seaside Ballroom
Same, Same But Different: Recovering Neural Network Quantization Error Through Weight Factorization
In Applications: Computer Vision
Eldad Meller · Alexander Finkelstein · Uri Almog · Mark Grobman
[ Slides]  [ Video] 
Oral
Tue Jun 11 04:25 PM -- 04:30 PM (PDT) @ Hall B
Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search
In Deep Learning Optimization
Youhei Akimoto · Shinichi Shirakawa · Nozomu Yoshinari · Kento Uchida · Shota Saito · Kouhei Nishida
[ Slides]  [ Video] 
Oral
Tue Jun 11 04:25 PM -- 04:30 PM (PDT) @ Room 201
Active Manifolds: A non-linear analogue to Active Subspaces
In General ML
Robert Bridges · Anthony Gruber · Christopher Felder · Miki Verma · Chelsey Hoff
[ Slides]  [ Video] 
Oral
Tue Jun 11 04:30 PM -- 04:35 PM (PDT) @ Room 103
Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints
In Statistical Learning Theory
Andrew Cotter · Maya Gupta · Heinrich Jiang · Nati Srebro · Karthik Sridharan · Serena Wang · Blake Woodworth · Seungil You
[ Slides]  [ Video] 
Oral
Tue Jun 11 04:30 PM -- 04:35 PM (PDT) @ Seaside Ballroom
GDPP: Learning Diverse Generations using Determinantal Point Processes
In Applications: Computer Vision
Mohamed Elfeki · Camille Couprie · Morgane Riviere · Mohamed Elhoseiny
Oral
Tue Jun 11 04:30 PM -- 04:35 PM (PDT) @ Hall A
Shallow-Deep Networks: Understanding and Mitigating Network Overthinking
In Deep Learning Architectures
Yigitcan Kaya · Sanghyun Hong · Tudor Dumitras
[ Slides]  [ Video] 
Oral
Tue Jun 11 04:30 PM -- 04:35 PM (PDT) @ Room 201
Optimal Minimal Margin Maximization with Boosting
In General ML
Alexander Mathiasen · Kasper Green Larsen · Allan Grønlund
[ Slides]  [ Video] 
Oral
Tue Jun 11 04:30 PM -- 04:35 PM (PDT) @ Grand Ballroom
Scalable Fair Clustering
In Large Scale Learning and Systems
Arturs Backurs · Piotr Indyk · Krzysztof Onak · Baruch Schieber · Ali Vakilian · Tal Wagner
[ Slides]  [ Video] 
Oral
Tue Jun 11 04:30 PM -- 04:35 PM (PDT) @ Room 102
An Optimal Private Stochastic-MAB Algorithm based on Optimal Private Stopping Rule
In Privacy
Touqir Sajed · Or Sheffet
[ Slides] 
Oral
Tue Jun 11 04:30 PM -- 04:35 PM (PDT) @ Hall B
A Quantitative Analysis of the Effect of Batch Normalization on Gradient Descent
In Deep Learning Optimization
YongQiang Cai · Qianxiao Li · Zuowei Shen
[ Slides]  [ Video] 
Oral
Tue Jun 11 04:30 PM -- 04:35 PM (PDT) @ Room 104
Per-Decision Option Discounting
In Reinforcement Learning Theory
Anna Harutyunyan · Peter Vrancx · Philippe Hamel · Ann Nowe · Doina Precup
[ Slides]  [ Video] 
Oral
Tue Jun 11 04:30 PM -- 04:35 PM (PDT) @ Room 101
Neurally-Guided Structure Inference
In Bayesian Methods
Sidi Lu · Jiayuan Mao · Josh Tenenbaum · Jiajun Wu
[ Slides]  [ Video] 
Oral
Tue Jun 11 04:35 PM -- 04:40 PM (PDT) @ Grand Ballroom
Conditional Gradient Methods via Stochastic Path-Integrated Differential Estimator
In Large Scale Learning and Systems
Alp Yurtsever · Suvrit Sra · Volkan Cevher
[ Slides]  [ Video] 
Oral
Tue Jun 11 04:35 PM -- 04:40 PM (PDT) @ Room 103
Optimality Implies Kernel Sum Classifiers are Statistically Efficient
In Statistical Learning Theory
Raphael Meyer · Jean Honorio
[ Slides]  [ Video] 
Oral
Tue Jun 11 04:35 PM -- 04:40 PM (PDT) @ Hall B
The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study
In Deep Learning Optimization
Daniel Park · Jascha Sohl-Dickstein · Quoc Le · Samuel Smith
[ Slides]  [ Video] 
Oral
Tue Jun 11 04:35 PM -- 04:40 PM (PDT) @ Hall A
Graph U-Nets
In Deep Learning Architectures
Hongyang Gao · Shuiwang Ji
[ Slides]  [ Video] 
Oral
Tue Jun 11 04:35 PM -- 04:40 PM (PDT) @ Room 102
Sublinear Space Private Algorithms Under the Sliding Window Model
In Privacy
Jalaj Upadhyay
[ Slides]  [ Video] 
Oral
Tue Jun 11 04:35 PM -- 04:40 PM (PDT) @ Room 104
Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds
In Reinforcement Learning Theory
Andrea Zanette · Emma Brunskill
[ Slides]  [ Video] 
Oral
Tue Jun 11 04:35 PM -- 04:40 PM (PDT) @ Room 201
Generalized Linear Rule Models
In General ML
Dennis Wei · Sanjeeb Dash · Tian Gao · Oktay Gunluk
[ Slides]  [ Video] 
Oral
Tue Jun 11 04:35 PM -- 04:40 PM (PDT) @ Room 101
Bayesian Joint Spike-and-Slab Graphical Lasso
In Bayesian Methods
Zehang Li · Tyler Mccormick · Samuel Clark
[ Slides]  [ Video] 
Oral
Tue Jun 11 04:35 PM -- 04:40 PM (PDT) @ Seaside Ballroom
Co-Representation Network for Generalized Zero-Shot Learning
In Applications: Computer Vision
Fei Zhang · Guangming Shi
[ Slides] 
Oral
Tue Jun 11 04:40 PM -- 05:00 PM (PDT) @ Hall B
AdaGrad stepsizes: sharp convergence over nonconvex landscapes
In Deep Learning Optimization
Rachel Ward · Xiaoxia Wu · Leon Bottou
[ Slides]  [ Video] 
Oral
Tue Jun 11 04:40 PM -- 05:00 PM (PDT) @ Room 201
Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and Applications
In General ML
Pin-Yu Chen · Lingfei Wu · Sijia Liu · Indika Rajapakse
[ Slides]  [ Video] 
Oral
Tue Jun 11 04:40 PM -- 05:00 PM (PDT) @ Room 103
The Implicit Fairness Criterion of Unconstrained Learning
In Statistical Learning Theory
Lydia T. Liu · Max Simchowitz · Moritz Hardt
[ Video] 
Oral
Tue Jun 11 04:40 PM -- 05:00 PM (PDT) @ Room 102
Locally Private Bayesian Inference for Count Models
In Privacy
Aaron Schein · Steven Wu · Alexandra Schofield · Mingyuan Zhou · Hanna Wallach
[ Video] 
Oral
Tue Jun 11 04:40 PM -- 05:00 PM (PDT) @ Room 101
Rotation Invariant Householder Parameterization for Bayesian PCA
In Bayesian Methods
Rajbir-Singh Nirwan · Nils Bertschinger
[ Slides]  [ Video] 
Oral
Tue Jun 11 04:40 PM -- 05:00 PM (PDT) @ Hall A
SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver
In Deep Learning Architectures
Po-Wei Wang · Priya Donti · Bryan Wilder · Zico Kolter
[ Slides]  [ Video] 
Oral
Tue Jun 11 04:40 PM -- 05:00 PM (PDT) @ Room 104
A Theory of Regularized Markov Decision Processes
In Reinforcement Learning Theory
Matthieu Geist · Bruno Scherrer · Olivier Pietquin
[ Slides]  [ Video] 
Oral
Tue Jun 11 04:40 PM -- 05:00 PM (PDT) @ Grand Ballroom
Fault Tolerance in Iterative-Convergent Machine Learning
In Large Scale Learning and Systems
Aurick Qiao · Bryon Aragam · Bingjing Zhang · Eric Xing
[ Video] 
Oral
Tue Jun 11 04:40 PM -- 05:00 PM (PDT) @ Seaside Ballroom
GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects
In Applications: Computer Vision
Edward Smith · Edward Smith · Scott Fujimoto · Adriana Romero Soriano · Scott Fujimoto · Adriana Romero Soriano · David Meger · David Meger
[ Slides]  [ Video] 
Oral
Tue Jun 11 05:00 PM -- 05:05 PM (PDT) @ Seaside Ballroom
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
In Applications: Computer Vision
Mingxing Tan · Quoc Le
[ Slides]  [ Video] 
Oral
Tue Jun 11 05:00 PM -- 05:05 PM (PDT) @ Room 103
Weak Detection of Signal in the Spiked Wigner Model
In Statistical Learning Theory
Hye Won Chung · Ji Oon Lee
[ Slides]  [ Video] 
Oral
Tue Jun 11 05:00 PM -- 05:05 PM (PDT) @ Grand Ballroom
Static Automatic Batching In TensorFlow
In Large Scale Learning and Systems
Ashish Agarwal
[ Slides]  [ Video] 
Oral
Tue Jun 11 05:00 PM -- 05:05 PM (PDT) @ Room 101
A Framework for Bayesian Optimization in Embedded Subspaces
In Bayesian Methods
Amin Nayebi · Alexander Munteanu · Matthias Poloczek
[ Slides]  [ Video] 
Oral
Tue Jun 11 05:00 PM -- 05:05 PM (PDT) @ Room 104
Discovering Options for Exploration by Minimizing Cover Time
In Reinforcement Learning Theory
Yuu Jinnai · Jee Won Park · David Abel · George Konidaris
[ Slides]  [ Video] 
Oral
Tue Jun 11 05:00 PM -- 05:05 PM (PDT) @ Room 102
Low Latency Privacy Preserving Inference
In Privacy
Alon Brutzkus · Ran Gilad-Bachrach · Oren Elisha
[ Slides]  [ Video] 
Oral
Tue Jun 11 05:00 PM -- 05:05 PM (PDT) @ Hall A
Area Attention
In Deep Learning Architectures
Yang Li · Lukasz Kaiser · Samy Bengio · Si Si
[ Slides]  [ Video] 
Oral
Tue Jun 11 05:00 PM -- 05:05 PM (PDT) @ Room 201
Variational Inference for sparse network reconstruction from count data
In General ML
Julien Chiquet · Stephane Robin · Mahendra Mariadassou
[ Slides]  [ Video] 
Oral
Tue Jun 11 05:00 PM -- 05:05 PM (PDT) @ Hall B
Beyond Backprop: Online Alternating Minimization with Auxiliary Variables
In Deep Learning Optimization
Anna Choromanska · Benjamin Cowen · Sadhana Kumaravel · Ronny Luss · Mattia Rigotti · Irina Rish · Paolo DiAchille · Viatcheslav Gurev · Brian Kingsbury · Ravi Tejwani · Djallel Bouneffouf
[ Slides]  [ Video] 
Oral
Tue Jun 11 05:05 PM -- 05:10 PM (PDT) @ Hall B
SWALP : Stochastic Weight Averaging in Low Precision Training
In Deep Learning Optimization
Guandao Yang · Tianyi Zhang · Polina Kirichenko · Junwen Bai · Andrew Wilson · Christopher De Sa
[ Slides]  [ Video] 
Oral
Tue Jun 11 05:05 PM -- 05:10 PM (PDT) @ Room 103
Rademacher Complexity for Adversarially Robust Generalization
In Statistical Learning Theory
Dong Yin · Kannan Ramchandran · Peter Bartlett
[ Slides]  [ Video] 
Oral
Tue Jun 11 05:05 PM -- 05:10 PM (PDT) @ Room 201
Simplifying Graph Convolutional Networks
In General ML
Felix Wu · Amauri Souza · Tianyi Zhang · Christopher Fifty · Tao Yu · Kilian Weinberger
[ Slides]  [ Video] 
Oral
Tue Jun 11 05:05 PM -- 05:10 PM (PDT) @ Seaside Ballroom
Geometry Aware Convolutional Filters for Omnidirectional Images Representation
In Applications: Computer Vision
Renata Khasanova · Pascal Frossard
[ Slides]  [ Video] 
Oral
Tue Jun 11 05:05 PM -- 05:10 PM (PDT) @ Room 104
Policy Certificates: Towards Accountable Reinforcement Learning
In Reinforcement Learning Theory
Christoph Dann · Lihong Li · Wei Wei · Emma Brunskill
[ Slides]  [ Video] 
Oral
Tue Jun 11 05:05 PM -- 05:10 PM (PDT) @ Room 102
Communication Complexity in Locally Private Distribution Estimation and Heavy Hitters
In Privacy
Jayadev Acharya · Ziteng Sun
[ Slides]  [ Video] 
Oral
Tue Jun 11 05:05 PM -- 05:10 PM (PDT) @ Grand Ballroom
Improving Neural Network Quantization without Retraining using Outlier Channel Splitting
In Large Scale Learning and Systems
Ritchie Zhao · Yuwei Hu · Jordan Dotzel · Christopher De Sa · Zhiru Zhang
[ Slides]  [ Video] 
Oral
Tue Jun 11 05:05 PM -- 05:10 PM (PDT) @ Room 101
Convolutional Poisson Gamma Belief Network
In Bayesian Methods
CHAOJIE WANG · Bo Chen · SUCHENG XIAO · Mingyuan Zhou
[ Slides]  [ Video] 
Oral
Tue Jun 11 05:05 PM -- 05:10 PM (PDT) @ Hall A
The Evolved Transformer
In Deep Learning Architectures
David So · Quoc Le · Chen Liang
[ Slides]  [ Video] 
Oral
Tue Jun 11 05:10 PM -- 05:15 PM (PDT) @ Room 104
Action Robust Reinforcement Learning and Applications in Continuous Control
In Reinforcement Learning Theory
Chen Tessler · Chen Tessler · Yonathan Efroni · Yonathan Efroni · Shie Mannor · Shie Mannor
[ Slides]  [ Video] 
Oral
Tue Jun 11 05:10 PM -- 05:15 PM (PDT) @ Hall B
Efficient optimization of loops and limits with randomized telescoping sums
In Deep Learning Optimization
Alex Beatson · Ryan P Adams
[ Slides]  [ Video] 
Oral
Tue Jun 11 05:10 PM -- 05:15 PM (PDT) @ Room 103
Provably efficient RL with Rich Observations via Latent State Decoding
In Statistical Learning Theory
Simon Du · Akshay Krishnamurthy · Nan Jiang · Alekh Agarwal · Miroslav Dudik · John Langford
[ Slides]  [ Video] 
Oral
Tue Jun 11 05:10 PM -- 05:15 PM (PDT) @ Room 101
Automatic Posterior Transformation for Likelihood-Free Inference
In Bayesian Methods
David Greenberg · Marcel Nonnenmacher · Jakob Macke
[ Slides]  [ Video] 
Oral
Tue Jun 11 05:10 PM -- 05:15 PM (PDT) @ Room 102
Poission Subsampled R\'enyi Differential Privacy
In Privacy
Yuqing Zhu · Yu-Xiang Wang
[ Slides]  [ Video] 
Oral
Tue Jun 11 05:10 PM -- 05:15 PM (PDT) @ Room 201
Robust Influence Maximization for Hyperparametric Models
In General ML
Dimitrios Kalimeris · Gal Kaplun · Yaron Singer
[ Slides]  [ Video] 
Oral
Tue Jun 11 05:10 PM -- 05:15 PM (PDT) @ Hall A
Jumpout : Improved Dropout for Deep Neural Networks with ReLUs
In Deep Learning Architectures
Shengjie Wang · Tianyi Zhou · Jeff Bilmes
[ Slides]  [ Video] 
Oral
Tue Jun 11 05:10 PM -- 05:15 PM (PDT) @ Grand Ballroom
Memory-Optimal Direct Convolutions for Maximizing Classification Accuracy in Embedded Applications
In Large Scale Learning and Systems
Albert Gural · Boris Murmann
[ Slides]  [ Video] 
Oral
Tue Jun 11 05:10 PM -- 05:15 PM (PDT) @ Seaside Ballroom
A Personalized Affective Memory Model for Improving Emotion Recognition
In Applications: Computer Vision
Pablo Barros · German Parisi · Stefan Wermter
[ Slides]  [ Video] 
Oral
Tue Jun 11 05:15 PM -- 05:20 PM (PDT) @ Room 101
Active Learning for Decision-Making from Imbalanced Observational Data
In Bayesian Methods
Iiris Sundin · Peter Schulam · Eero Siivola · Aki Vehtari · Suchi Saria · Samuel Kaski
[ Slides]  [ Video] 
Oral
Tue Jun 11 05:15 PM -- 05:20 PM (PDT) @ Seaside Ballroom
Temporal Gaussian Mixture Layer for Videos
In Applications: Computer Vision
AJ Piergiovanni · Michael Ryoo
[ Slides]  [ Video] 
Oral
Tue Jun 11 05:15 PM -- 05:20 PM (PDT) @ Hall A
Stochastic Deep Networks
In Deep Learning Architectures
Gwendoline De Bie · Gabriel Peyré · Marco Cuturi
[ Slides]  [ Video] 
Oral
Tue Jun 11 05:15 PM -- 05:20 PM (PDT) @ Hall B
Self-similar Epochs: Value in arrangement
In Deep Learning Optimization
Eliav Buchnik · Edith Cohen · Avinatan Hasidim · Yossi Matias
[ Slides]  [ Video] 
Oral
Tue Jun 11 05:15 PM -- 05:20 PM (PDT) @ Room 201
HyperGAN: A Generative Model for Diverse, Performant Neural Networks
In General ML
Neale Ratzlaff · Fuxin Li
[ Slides]  [ Video] 
Oral
Tue Jun 11 05:15 PM -- 05:20 PM (PDT) @ Room 104
The Value Function Polytope in Reinforcement Learning
In Reinforcement Learning Theory
Robert Dadashi · Marc Bellemare · Adrien Ali Taiga · Nicolas Le Roux · Dale Schuurmans
[ Slides]  [ Video] 
Oral
Tue Jun 11 05:15 PM -- 05:20 PM (PDT) @ Room 102
Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCA
In Privacy
Jordan Awan · Ana Kenney · Matthew Reimherr · Aleksandra Slavković
[ Slides]  [ Video] 
Oral
Tue Jun 11 05:15 PM -- 05:20 PM (PDT) @ Grand Ballroom
DL2: Training and Querying Neural Networks with Logic
In Large Scale Learning and Systems
Marc Fischer · Mislav Balunovic · Dana Drachsler-Cohen · Timon Gehr · Ce Zhang · Martin Vechev
[ Slides]  [ Video] 
Oral
Tue Jun 11 05:15 PM -- 05:20 PM (PDT) @ Room 103
Information-Theoretic Considerations in Batch Reinforcement Learning
In Statistical Learning Theory
Jinglin Chen · Nan Jiang
[ Slides]  [ Video] 
Break
Tue Jun 11 05:30 PM -- 06:00 PM (PDT)
Light Evening Snack
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #1
SelectiveNet: A Deep Neural Network with an Integrated Reject Option
In Posters Tue
Yonatan Geifman · Ran El-Yaniv
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #2
Manifold Mixup: Better Representations by Interpolating Hidden States
In Posters Tue
Vikas Verma · Alex Lamb · Christopher Beckham · Amir Najafi · Ioannis Mitliagkas · David Lopez-Paz · Yoshua Bengio
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #3
Processing Megapixel Images with Deep Attention-Sampling Models
In Posters Tue
Angelos Katharopoulos · Francois Fleuret
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #4
TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning
In Posters Tue
Sung Whan Yoon · Jun Seo · Jaekyun Moon
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #5
Online Meta-Learning
In Posters Tue
Chelsea Finn · Aravind Rajeswaran · Sham Kakade · Sergey Levine
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #6
Training Neural Networks with Local Error Signals
In Posters Tue
Arild Nøkland · Lars Hiller Eidnes
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #7
GMNN: Graph Markov Neural Networks
In Posters Tue
Meng Qu · Yoshua Bengio · Jian Tang
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #8
Self-Attention Graph Pooling
In Posters Tue
Junhyun Lee · Inyeop Lee · Jaewoo Kang
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #9
Combating Label Noise in Deep Learning using Abstention
In Posters Tue
Sunil Thulasidasan · Tanmoy Bhattacharya · Jeff Bilmes · Gopinath Chennupati · Jamal Mohd-Yusof
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #10
LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning
In Posters Tue
Huaiyu Li · Weiming Dong · Xing Mei · Chongyang Ma · Feiyue Huang · Bao-Gang Hu
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #11
Self-Attention Generative Adversarial Networks
In Posters Tue
Han Zhang · Ian Goodfellow · Dimitris Metaxas · Augustus Odena
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #12
Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution Matching
In Posters Tue
Ziliang Chen · ZHANFU YANG · Xiaoxi Wang · Xiaodan Liang · xiaopeng yan · Guanbin Li · Liang Lin
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #13
High-Fidelity Image Generation With Fewer Labels
In Posters Tue
Mario Lucic · Michael Tschannen · Marvin Ritter · Xiaohua Zhai · Olivier Bachem · Sylvain Gelly
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #14
Revisiting precision recall definition for generative modeling
In Posters Tue
Loic Simon · Ryan Webster · Julien Rabin
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #15
Wasserstein of Wasserstein Loss for Learning Generative Models
In Posters Tue
Yonatan Dukler · Wuchen Li · Alex Lin · Guido Montufar
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #16
Flat Metric Minimization with Applications in Generative Modeling
In Posters Tue
Thomas Möllenhoff · Daniel Cremers
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #17
Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs
In Posters Tue
Yogesh Balaji · Hamed Hassani · Rama Chellappa · Soheil Feizi
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #18
Non-Parametric Priors For Generative Adversarial Networks
In Posters Tue
Rajhans Singh · Pavan Turaga · Suren Jayasuriya · Ravi Garg · Martin Braun
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #19
Lipschitz Generative Adversarial Nets
In Posters Tue
Zhiming Zhou · Jiadong Liang · Yuxuan Song · Lantao Yu · Hongwei Wang · Weinan Zhang · Yong Yu · Zhihua Zhang
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #20
HexaGAN: Generative Adversarial Nets for Real World Classification
In Posters Tue
Uiwon Hwang · Dahuin Jung · Sungroh Yoon
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #21
Graph Matching Networks for Learning the Similarity of Graph Structured Objects
In Posters Tue
Yujia Li · Chenjie Gu · Thomas Dullien · Oriol Vinyals · Pushmeet Kohli
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #22
BayesNAS: A Bayesian Approach for Neural Architecture Search
In Posters Tue
Hongpeng Zhou · Minghao Yang · Jun Wang · Wei Pan
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #23
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks
In Posters Tue
Juho Lee · Yoonho Lee · Jungtaek Kim · Adam Kosiorek · Seungjin Choi · Yee-Whye Teh
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #24
Shallow-Deep Networks: Understanding and Mitigating Network Overthinking
In Posters Tue
Yigitcan Kaya · Sanghyun Hong · Tudor Dumitras
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #25
Graph U-Nets
In Posters Tue
Hongyang Gao · Shuiwang Ji
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #26
SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver
In Posters Tue
Po-Wei Wang · Priya Donti · Bryan Wilder · Zico Kolter
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #27
Area Attention
In Posters Tue
Yang Li · Lukasz Kaiser · Samy Bengio · Si Si
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #28
The Evolved Transformer
In Posters Tue
David So · Quoc Le · Chen Liang
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #29
Jumpout : Improved Dropout for Deep Neural Networks with ReLUs
In Posters Tue
Shengjie Wang · Tianyi Zhou · Jeff Bilmes
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #30
Stochastic Deep Networks
In Posters Tue
Gwendoline De Bie · Gabriel Peyré · Marco Cuturi
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #31
ELF OpenGo: an analysis and open reimplementation of AlphaZero
In Posters Tue
Yuandong Tian · Jerry Ma · Qucheng Gong · Shubho Sengupta · Zhuoyuan Chen · James Pinkerton · Larry Zitnick
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #32
Making Deep Q-learning methods robust to time discretization
In Posters Tue
Corentin Tallec · Leonard Blier · Yann Ollivier
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #33
Nonlinear Distributional Gradient Temporal-Difference Learning
In Posters Tue
chao qu · Shie Mannor · Huan Xu
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #34
Composing Entropic Policies using Divergence Correction
In Posters Tue
Jonathan Hunt · Andre Barreto · Timothy Lillicrap · Nicolas Heess
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #35
TibGM: A Transferable and Information-Based Graphical Model Approach for Reinforcement Learning
In Posters Tue
Tameem Adel · Adrian Weller
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #36
Multi-Agent Adversarial Inverse Reinforcement Learning
In Posters Tue
Lantao Yu · Jiaming Song · Stefano Ermon
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #37
Policy Consolidation for Continual Reinforcement Learning
In Posters Tue
Christos Kaplanis · Murray Shanahan · Claudia Clopath
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #38
Off-Policy Deep Reinforcement Learning without Exploration
In Posters Tue
Scott Fujimoto · David Meger · Doina Precup
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #39
Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation
In Posters Tue
Ruohan Wang · Carlo Ciliberto · Pierluigi Vito Amadori · Yiannis Demiris
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #40
Revisiting the Softmax Bellman Operator: New Benefits and New Perspective
In Posters Tue
Zhao Song · Ron Parr · Lawrence Carin
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #41
An Investigation of Model-Free Planning
In Posters Tue
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 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #42
CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning
In Posters Tue
Cédric Colas · Pierre-Yves Oudeyer · Olivier Sigaud · Pierre Fournier · Mohamed Chetouani
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #43
Task-Agnostic Dynamics Priors for Deep Reinforcement Learning
In Posters Tue
Yilun Du · Karthik Narasimhan
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #44
Diagnosing Bottlenecks in Deep Q-learning Algorithms
In Posters Tue
Justin Fu · Aviral Kumar · Matthew Soh · Sergey Levine
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #45
Collaborative Evolutionary Reinforcement Learning
In Posters Tue
Shauharda Khadka · Somdeb Majumdar · Tarek Nassar · Zach Dwiel · Evren Tumer · Santiago Miret · Yinyin Liu · Kagan Tumer
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #46
EMI: Exploration with Mutual Information
In Posters Tue
Hyoungseok Kim · Jaekyeom Kim · Yeonwoo Jeong · Sergey Levine · Hyun Oh Song
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #47
Imitation Learning from Imperfect Demonstration
In Posters Tue
Yueh-Hua Wu · Nontawat Charoenphakdee · Han Bao · Voot Tangkaratt · Masashi Sugiyama
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #48
Curiosity-Bottleneck: Exploration By Distilling Task-Specific Novelty
In Posters Tue
Youngjin Kim · Daniel Nam · Hyunwoo Kim · Ji-Hoon Kim · Gunhee Kim
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #49
Dynamic Weights in Multi-Objective Deep Reinforcement Learning
In Posters Tue
Axel Abels · Diederik Roijers · Tom Lenaerts · Ann Nowé · Denis Steckelmacher
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #50
Fingerprint Policy Optimisation for Robust Reinforcement Learning
In Posters Tue
Supratik Paul · Michael A Osborne · Shimon Whiteson
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #51
An Investigation into Neural Net Optimization via Hessian Eigenvalue Density
In Posters Tue
Behrooz Ghorbani · Shankar Krishnan · Ying Xiao
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #52
Differentiable Linearized ADMM
In Posters Tue
Xingyu Xie · Jianlong Wu · Guangcan Liu · Zhisheng Zhong · Zhouchen Lin
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #53
Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search
In Posters Tue
Youhei Akimoto · Shinichi Shirakawa · Nozomu Yoshinari · Kento Uchida · Shota Saito · Kouhei Nishida
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #54
A Quantitative Analysis of the Effect of Batch Normalization on Gradient Descent
In Posters Tue
YongQiang Cai · Qianxiao Li · Zuowei Shen
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #55
The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study
In Posters Tue
Daniel Park · Jascha Sohl-Dickstein · Quoc Le · Samuel Smith
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #56
AdaGrad stepsizes: sharp convergence over nonconvex landscapes
In Posters Tue
Rachel Ward · Xiaoxia Wu · Leon Bottou
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #57
Beyond Backprop: Online Alternating Minimization with Auxiliary Variables
In Posters Tue
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 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #58
SWALP : Stochastic Weight Averaging in Low Precision Training
In Posters Tue
Guandao Yang · Tianyi Zhang · Polina Kirichenko · Junwen Bai · Andrew Wilson · Christopher De Sa
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #59
Efficient optimization of loops and limits with randomized telescoping sums
In Posters Tue
Alex Beatson · Ryan P Adams
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #60
Self-similar Epochs: Value in arrangement
In Posters Tue
Eliav Buchnik · Edith Cohen · Avinatan Hasidim · Yossi Matias
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #61
Adversarial Attacks on Node Embeddings via Graph Poisoning
In Posters Tue
Aleksandar Bojchevski · Stephan Günnemann
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #62
First-Order Adversarial Vulnerability of Neural Networks and Input Dimension
In Posters Tue
Carl-Johann Simon-Gabriel · Yann Ollivier · Leon Bottou · Bernhard Schölkopf · David Lopez-Paz
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #63
On Certifying Non-Uniform Bounds against Adversarial Attacks
In Posters Tue
Chen Liu · Ryota Tomioka · Volkan Cevher
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #64
Improving Adversarial Robustness via Promoting Ensemble Diversity
In Posters Tue
Tianyu Pang · Kun Xu · Chao Du · Ning Chen · Jun Zhu
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #65
Adversarial camera stickers: A physical camera-based attack on deep learning systems
In Posters Tue
Juncheng Li · Frank R Schmidt · Zico Kolter
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #66
Adversarial examples from computational constraints
In Posters Tue
Sebastien Bubeck · Yin Tat Lee · Eric Price · Ilya Razenshteyn
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #67
POPQORN: Quantifying Robustness of Recurrent Neural Networks
In Posters Tue
CHING-YUN KO · Zhaoyang Lyu · Tsui-Wei Weng · Luca Daniel · Ngai Wong · Dahua Lin
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #68
Using Pre-Training Can Improve Model Robustness and Uncertainty
In Posters Tue
Dan Hendrycks · Kimin Lee · Mantas Mazeika
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #69
Generalized No Free Lunch Theorem for Adversarial Robustness
In Posters Tue
Elvis Dohmatob
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #70
PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach
In Posters Tue
Tsui-Wei Weng · Pin-Yu Chen · Lam Nguyen · Mark Squillante · Akhilan Boopathy · Ivan Oseledets · Luca Daniel
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #71
On Learning Invariant Representations for Domain Adaptation
In Posters Tue
Han Zhao · Remi Tachet des Combes · Kun Zhang · Geoff Gordon
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #72
Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models
In Posters Tue
Mor Shpigel Nacson · Suriya Gunasekar · Jason Lee · Nati Srebro · Daniel Soudry
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #73
Adversarial Generation of Time-Frequency Features with application in audio synthesis
In Posters Tue
Andrés Marafioti · Nathanaël Perraudin · Nicki Holighaus · Piotr Majdak
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #74
On the Universality of Invariant Networks
In Posters Tue
Haggai Maron · Ethan Fetaya · Nimrod Segol · Yaron Lipman
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #75
Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks
In Posters Tue
Sanjeev Arora · Simon Du · Wei Hu · Zhiyuan Li · Ruosong Wang
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #76
Gauge Equivariant Convolutional Networks and the Icosahedral CNN
In Posters Tue
Taco Cohen · Maurice Weiler · Berkay Kicanaoglu · Max Welling
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #77
Feature-Critic Networks for Heterogeneous Domain Generalization
In Posters Tue
Yiying Li · Yongxin Yang · Wei Zhou · Timothy Hospedales
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #78
Learning to Convolve: A Generalized Weight-Tying Approach
In Posters Tue
Nichita Diaconu · Daniel E Worrall
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #79
On Dropout and Nuclear Norm Regularization
In Posters Tue
Poorya Mianjy · Raman Arora
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #80
Gradient Descent Finds Global Minima of Deep Neural Networks
In Posters Tue
Simon Du · Jason Lee · Haochuan Li · Liwei Wang · Xiyu Zhai
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #81
Composable Core-sets for Determinant Maximization: A Simple Near-Optimal Algorithm
In Posters Tue
Sepideh Mahabadi · Piotr Indyk · Shayan Oveis Gharan · Alireza Rezaei
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #82
Sublinear Time Nearest Neighbor Search over Generalized Weighted Space
In Posters Tue
Yifan Lei · Qiang Huang · Mohan Kankanhalli · Anthony Tung
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #83
Compressing Gradient Optimizers via Count-Sketches
In Posters Tue
Ryan Spring · Anastasios Kyrillidis · Vijai Mohan · Anshumali Shrivastava
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #84
Scalable Fair Clustering
In Posters Tue
Arturs Backurs · Piotr Indyk · Krzysztof Onak · Baruch Schieber · Ali Vakilian · Tal Wagner
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #85
Conditional Gradient Methods via Stochastic Path-Integrated Differential Estimator
In Posters Tue
Alp Yurtsever · Suvrit Sra · Volkan Cevher
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #86
Fault Tolerance in Iterative-Convergent Machine Learning
In Posters Tue
Aurick Qiao · Bryon Aragam · Bingjing Zhang · Eric Xing
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #87
Static Automatic Batching In TensorFlow
In Posters Tue
Ashish Agarwal
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #88
Improving Neural Network Quantization without Retraining using Outlier Channel Splitting
In Posters Tue
Ritchie Zhao · Yuwei Hu · Jordan Dotzel · Christopher De Sa · Zhiru Zhang
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #89
Memory-Optimal Direct Convolutions for Maximizing Classification Accuracy in Embedded Applications
In Posters Tue
Albert Gural · Boris Murmann
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #90
DL2: Training and Querying Neural Networks with Logic
In Posters Tue
Marc Fischer · Mislav Balunovic · Dana Drachsler-Cohen · Timon Gehr · Ce Zhang · Martin Vechev
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #91
PA-GD: On the Convergence of Perturbed Alternating Gradient Descent to Second-Order Stationary Points for Structured Nonconvex Optimization
In Posters Tue
Songtao Lu · Mingyi Hong · Zhengdao Wang
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #92
Improved Zeroth-Order Variance Reduced Algorithms and Analysis for Nonconvex Optimization
In Posters Tue
Kaiyi Ji · Zhe Wang · Yi Zhou · Yingbin LIANG
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #93
Faster Stochastic Alternating Direction Method of Multipliers for Nonconvex Optimization
In Posters Tue
Feihu Huang · Songcan Chen · Heng Huang
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #94
Lower Bounds for Smooth Nonconvex Finite-Sum Optimization
In Posters Tue
Dongruo Zhou · Quanquan Gu
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #95
Nonconvex Variance Reduced Optimization with Arbitrary Sampling
In Posters Tue
Samuel Horvath · Peter Richtarik
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #96
Error Feedback Fixes SignSGD and other Gradient Compression Schemes
In Posters Tue
Sai Praneeth Reddy Karimireddy · Quentin Rebjock · Sebastian Stich · Martin Jaggi
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #97
A Composite Randomized Incremental Gradient Method
In Posters Tue
Junyu Zhang · Lin Xiao
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #98
Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field Inference
In Posters Tue
Yatao Bian · Joachim Buhmann · Andreas Krause
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #99
Multiplicative Weights Updates as a distributed constrained optimization algorithm: Convergence to second-order stationary points almost always
In Posters Tue
Ioannis Panageas · Georgios Piliouras · xiao wang
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #100
Katalyst: Boosting Convex Katayusha for Non-Convex Problems with a Large Condition Number
In Posters Tue
Zaiyi Chen · Yi Xu · Haoyuan Hu · Tianbao Yang
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #101
Safe Policy Improvement with Baseline Bootstrapping
In Posters Tue
Romain Laroche · Paul TRICHELAIR · Remi Tachet des Combes
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #102
Distributional Reinforcement Learning for Efficient Exploration
In Posters Tue
Borislav Mavrin · Hengshuai Yao · Linglong Kong · Kaiwen Wu · Yaoliang Yu
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #103
Optimistic Policy Optimization via Multiple Importance Sampling
In Posters Tue
Matteo Papini · Alberto Maria Metelli · Lorenzo Lupo · Marcello Restelli
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #104
Neural Logic Reinforcement Learning
In Posters Tue
zhengyao jiang · Shan Luo
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #105
Learning to Collaborate in Markov Decision Processes
In Posters Tue
Goran Radanovic · Rati Devidze · David Parkes · Adish Singla
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #106
Predictor-Corrector Policy Optimization
In Posters Tue
Ching-An Cheng · Xinyan Yan · Nathan Ratliff · Byron Boots
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #107
Learning a Prior over Intent via Meta-Inverse Reinforcement Learning
In Posters Tue
Kelvin Xu · Ellis Ratner · Anca Dragan · Sergey Levine · Chelsea Finn
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #108
DeepMDP: Learning Continuous Latent Space Models for Representation Learning
In Posters Tue
Carles Gelada · Saurabh Kumar · Jacob Buckman · Ofir Nachum · Marc Bellemare
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #109
Importance Sampling Policy Evaluation with an Estimated Behavior Policy
In Posters Tue
Josiah Hanna · Scott Niekum · Peter Stone
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #110
Learning from a Learner
In Posters Tue
alexis jacq · Matthieu Geist · Ana Paiva · Olivier Pietquin
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #111
Separable value functions across time-scales
In Posters Tue
Joshua Romoff · Peter Henderson · Ahmed Touati · Yann Ollivier · Joelle Pineau · Emma Brunskill
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #112
Learning Action Representations for Reinforcement Learning
In Posters Tue
Yash Chandak · Georgios Theocharous · James Kostas · Scott Jordan · Philip Thomas
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #113
Bayesian Counterfactual Risk Minimization
In Posters Tue
Ben London · Ted Sandler
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #114
Per-Decision Option Discounting
In Posters Tue
Anna Harutyunyan · Peter Vrancx · Philippe Hamel · Ann Nowe · Doina Precup
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #115
Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds
In Posters Tue
Andrea Zanette · Emma Brunskill
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #116
A Theory of Regularized Markov Decision Processes
In Posters Tue
Matthieu Geist · Bruno Scherrer · Olivier Pietquin
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #117
Discovering Options for Exploration by Minimizing Cover Time
In Posters Tue
Yuu Jinnai · Jee Won Park · David Abel · George Konidaris
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #118
Policy Certificates: Towards Accountable Reinforcement Learning
In Posters Tue
Christoph Dann · Lihong Li · Wei Wei · Emma Brunskill
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #119
The Value Function Polytope in Reinforcement Learning
In Posters Tue
Robert Dadashi · Marc Bellemare · Adrien Ali Taiga · Nicolas Le Roux · Dale Schuurmans
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #120
Data Shapley: Equitable Valuation of Data for Machine Learning
In Posters Tue
Amirata Ghorbani · James Zou
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #121
Feature Grouping as a Stochastic Regularizer for High-Dimensional Structured Data
In Posters Tue
Sergul Aydore · Thirion Bertrand · Gael Varoquaux
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #122
Metric-Optimized Example Weights
In Posters Tue
Sen Zhao · Mahdi Milani Fard · Harikrishna Narasimhan · Maya Gupta
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #123
Improving Model Selection by Employing the Test Data
In Posters Tue
Max Westphal · Werner Brannath
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #124
Topological Data Analysis of Decision Boundaries with Application to Model Selection
In Posters Tue
Karthikeyan Ramamurthy · Kush Varshney · Krishnan Mody
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #125
Contextual Memory Trees
In Posters Tue
Wen Sun · Alina Beygelzimer · Hal Daumé III · John Langford · Paul Mineiro
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #126
Sparse Extreme Multi-label Learning with Oracle Property
In Posters Tue
Weiwei Liu · Xiaobo Shen
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #127
Shape Constraints for Set Functions
In Posters Tue
Andrew Cotter · Maya Gupta · Heinrich Jiang · Erez Louidor · James Muller · Taman Narayan · Serena Wang · Tao Zhu
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #128
On The Power of Curriculum Learning in Training Deep Networks
In Posters Tue
Guy Hacohen · Daphna Weinshall
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #129
Voronoi Boundary Classification: A High-Dimensional Geometric Approach via Weighted Monte Carlo Integration
In Posters Tue
Vladislav Polianskii · Florian T. Pokorny
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #130
Robust Decision Trees Against Adversarial Examples
In Posters Tue
Hongge Chen · Huan Zhang · Duane Boning · Cho-Jui Hsieh
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #131
Automatic Classifiers as Scientific Instruments: One Step Further Away from Ground-Truth
In Posters Tue
Jacob Whitehill · Anand Ramakrishnan
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #132
Look Ma, No Latent Variables: Accurate Cutset Networks via Compilation
In Posters Tue
Tahrima Rahman · Shasha Jin · Vibhav Gogate
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #133
Optimal Transport for structured data with application on graphs
In Posters Tue
Titouan Vayer · Nicolas Courty · Romain Tavenard · Chapel Laetitia · Remi Flamary
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #134
Learning Optimal Linear Regularizers
In Posters Tue
Matthew Streeter
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #135
On Symmetric Losses for Learning from Corrupted Labels
In Posters Tue
Nontawat Charoenphakdee · Jongyeong Lee · Masashi Sugiyama
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #136
AUCµ: A Performance Metric for Multi-Class Machine Learning Models
In Posters Tue
Ross Kleiman · University of Wisconsin David Page
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #137
Regularization in directable environments with application to Tetris
In Posters Tue
Jan Malte Lichtenberg · Ozgur Simsek
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #138
Improved Dynamic Graph Learning through Fault-Tolerant Sparsification
In Posters Tue
Chunjiang Zhu · Sabine Storandt · Kam-Yiu Lam · Song Han · Jinbo Bi
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #139
Heterogeneous Model Reuse via Optimizing Multiparty Multiclass Margin
In Posters Tue
Xi-Zhu Wu · Song Liu · Zhi-Hua Zhou
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #140
Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff
In Posters Tue
Yochai Blau · Tomer Michaeli
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #141
Collaborative Channel Pruning for Deep Networks
In Posters Tue
Hanyu Peng · Jiaxiang Wu · Shifeng Chen · Junzhou Huang
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #142
Same, Same But Different: Recovering Neural Network Quantization Error Through Weight Factorization
In Posters Tue
Eldad Meller · Alexander Finkelstein · Uri Almog · Mark Grobman
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #143
GDPP: Learning Diverse Generations using Determinantal Point Processes
In Posters Tue
Mohamed Elfeki · Camille Couprie · Morgane Riviere · Mohamed Elhoseiny
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #144
Co-Representation Network for Generalized Zero-Shot Learning
In Posters Tue
Fei Zhang · Guangming Shi
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #145
GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects
In Posters Tue
Edward Smith · Scott Fujimoto · Adriana Romero Soriano · David Meger
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #146
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
In Posters Tue
Mingxing Tan · Quoc Le
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #147
Geometry Aware Convolutional Filters for Omnidirectional Images Representation
In Posters Tue
Renata Khasanova · Pascal Frossard
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #148
A Personalized Affective Memory Model for Improving Emotion Recognition
In Posters Tue
Pablo Barros · German Parisi · Stefan Wermter
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #149
Temporal Gaussian Mixture Layer for Videos
In Posters Tue
AJ Piergiovanni · Michael Ryoo
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #150
Regret Circuits: Composability of Regret Minimizers
In Posters Tue
Gabriele Farina · Christian Kroer · Tuomas Sandholm
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #151
Game Theoretic Optimization via Gradient-based Nikaido-Isoda Function
In Posters Tue
Arvind Raghunathan · Anoop Cherian · Devesh Jha
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #152
Stable-Predictive Optimistic Counterfactual Regret Minimization
In Posters Tue
Gabriele Farina · Christian Kroer · Noam Brown · Tuomas Sandholm
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #153
When Samples Are Strategically Selected
In Posters Tue
Hanrui Zhang · Yu Cheng · Vincent Conitzer
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #154
Statistical Foundations of Virtual Democracy
In Posters Tue
Anson Kahng · Min Kyung Lee · Ritesh Noothigattu · Ariel Procaccia · Christos-Alexandros Psomas
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #155
Optimal Auctions through Deep Learning
In Posters Tue
Paul Duetting · Zhe Feng · Harikrishna Narasimhan · David Parkes · Sai Srivatsa Ravindranath
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #156
Learning to Clear the Market
In Posters Tue
Weiran Shen · Sébastien Lahaie · Renato Leme
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #157
Learning to bid in revenue-maximizing auctions
In Posters Tue
Thomas Nedelec · Noureddine El Karoui · Vianney Perchet
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #158
Open-ended learning in symmetric zero-sum games
In Posters Tue
David Balduzzi · Marta Garnelo · Yoram Bachrach · Wojciech Czarnecki · Julien Perolat · Max Jaderberg · Thore Graepel
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #159
Deep Counterfactual Regret Minimization
In Posters Tue
Noam Brown · Adam Lerer · Sam Gross · Tuomas Sandholm
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #160
Generalized Approximate Survey Propagation for High-Dimensional Estimation
In Posters Tue
Carlo Lucibello · Luca Saglietti · Yue Lu
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #161
Boosted Density Estimation Remastered
In Posters Tue
Zac Cranko · Richard Nock
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #162
Inference and Sampling of $K_{33}$-free Ising Models
In Posters Tue
Valerii Likhosherstov · Yury Maximov · Misha Chertkov
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #163
Random Matrix Improved Covariance Estimation for a Large Class of Metrics
In Posters Tue
Malik TIOMOKO A · Romain Couillet · Florent BOUCHARD · Guillaume GINOLHAC
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #164
Dual Entangled Polynomial Code: Three-Dimensional Coding for Distributed Matrix Multiplication
In Posters Tue
Pedro Soto · Jun Li · Xiaodi Fan
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #165
Neural Joint Source-Channel Coding
In Posters Tue
Kristy Choi · Kedar Tatwawadi · Aditya Grover · Tsachy Weissman · Stefano Ermon
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #166
Doubly-Competitive Distribution Estimation
In Posters Tue
Yi Hao · Alon Orlitsky
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #167
Homomorphic Sensing
In Posters Tue
Manolis Tsakiris · Liangzu Peng
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #168
Phaseless PCA: Low-Rank Matrix Recovery from Column-wise Phaseless Measurements
In Posters Tue
Seyedehsara Nayer · Praneeth Narayanamurthy · Namrata Vaswani
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #169
Rate Distortion For Model Compression:From Theory To Practice
In Posters Tue
Weihao Gao · Yu-Han Liu · Chong Wang · Sewoong Oh
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #170
Formal Privacy for Functional Data with Gaussian Perturbations
In Posters Tue
Ardalan Mirshani · Matthew Reimherr · Aleksandra Slavković
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #171
Graphical-model based estimation and inference for differential privacy
In Posters Tue
Ryan McKenna · Daniel Sheldon · Gerome Miklau
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #172
White-box vs Black-box: Bayes Optimal Strategies for Membership Inference
In Posters Tue
Alexandre Sablayrolles · Douze Matthijs · Cordelia Schmid · Yann Ollivier · Herve Jegou
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #173
An Optimal Private Stochastic-MAB Algorithm based on Optimal Private Stopping Rule
In Posters Tue
Touqir Sajed · Or Sheffet
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #174
Sublinear Space Private Algorithms Under the Sliding Window Model
In Posters Tue
Jalaj Upadhyay
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #175
Locally Private Bayesian Inference for Count Models
In Posters Tue
Aaron Schein · Steven Wu · Alexandra Schofield · Mingyuan Zhou · Hanna Wallach
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #176
Low Latency Privacy Preserving Inference
In Posters Tue
Alon Brutzkus · Ran Gilad-Bachrach · Oren Elisha
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #177
Communication Complexity in Locally Private Distribution Estimation and Heavy Hitters
In Posters Tue
Jayadev Acharya · Ziteng Sun
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #178
Poission Subsampled R\'enyi Differential Privacy
In Posters Tue
Yuqing Zhu · Yu-Xiang Wang
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #179
Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCA
In Posters Tue
Jordan Awan · Ana Kenney · Matthew Reimherr · Aleksandra Slavković
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #180
Refined Complexity of PCA with Outliers
In Posters Tue
Kirill Simonov · Fedor Fomin · Petr Golovach · Fahad Panolan
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #181
On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms
In Posters Tue
Tianyi Lin · Nhat Ho · Michael Jordan
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #182
Passed & Spurious: Descent Algorithms and Local Minima in Spiked Matrix-Tensor Models
In Posters Tue
Stefano Sarao Mannelli · Florent Krzakala · Pierfrancesco Urbani · Lenka Zdeborova
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #183
Teaching a black-box learner
In Posters Tue
Sanjoy Dasgupta · Daniel Hsu · Stefanos Poulis · Jerry Zhu
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #184
PAC Learnability of Node Functions in Networked Dynamical Systems
In Posters Tue
Abhijin Adiga · Chris J Kuhlman · Madhav Marathe · S. S. Ravi · Anil Vullikanti
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #185
Online learning with kernel losses
In Posters Tue
Niladri Chatterji · Aldo Pacchiano · Peter Bartlett
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #186
Nearest Neighbor and Kernel Survival Analysis: Nonasymptotic Error Bounds and Strong Consistency Rates
In Posters Tue
George Chen
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #187
Fast Rates for a kNN Classifier Robust to Unknown Asymmetric Label Noise
In Posters Tue
Henry Reeve · Ata Kaban
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #188
Uniform Convergence Rate of the Kernel Density Estimator Adaptive to Intrinsic Volume Dimension
In Posters Tue
Jisu Kim · Jaehyeok Shin · Alessandro Rinaldo · Larry Wasserman
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #189
Maximum Likelihood Estimation for Learning Populations of Parameters
In Posters Tue
Ramya Korlakai Vinayak · Weihao Kong · Gregory Valiant · Sham Kakade
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #190
Projection onto Minkowski Sums with Application to Constrained Learning
In Posters Tue
Joong-Ho (Johann) Won · Jason Xu · Kenneth Lange
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #191
Blended Conditonal Gradients
In Posters Tue
Gábor Braun · Sebastian Pokutta · Dan Tu · Stephen Wright
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #192
Acceleration of SVRG and Katyusha X by Inexact Preconditioning
In Posters Tue
Yanli Liu · Fei Feng · Wotao Yin
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #193
Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD
In Posters Tue
Marten van Dijk · Lam Nguyen · PHUONG_HA NGUYEN · Dzung Phan
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #194
A Conditional-Gradient-Based Augmented Lagrangian Framework
In Posters Tue
Alp Yurtsever · Olivier Fercoq · Volkan Cevher
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #195
SGD: General Analysis and Improved Rates
In Posters Tue
Robert Gower · Nicolas Loizou · Xun Qian · Alibek Sailanbayev · Egor Shulgin · Peter Richtarik
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #196
Curvature-Exploiting Acceleration of Elastic Net Computations
In Posters Tue
Vien Mai · Mikael Johansson
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #197
Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication
In Posters Tue
Anastasiia Koloskova · Sebastian Stich · Martin Jaggi
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom
Safe Grid Search with Optimal Complexity
In Posters Tue
Eugene Ndiaye · Tam Le · Olivier Fercoq · Joseph Salmon · Ichiro Takeuchi
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #199
SAGA with Arbitrary Sampling
In Posters Tue
Xun Qian · Zheng Qu · Peter Richtarik
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #200
Natural Analysts in Adaptive Data Analysis
In Posters Tue
Tijana Zrnic · Moritz Hardt
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #201
CapsAndRuns: An Improved Method for Approximately Optimal Algorithm Configuration
In Posters Tue
Gellért Weisz · András György · Csaba Szepesvari
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #202
Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction
In Posters Tue
Giulia Luise · Dimitrios Stamos · Massimiliano Pontil · Carlo Ciliberto
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #203
Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints
In Posters Tue
Andrew Cotter · Maya Gupta · Heinrich Jiang · Nati Srebro · Karthik Sridharan · Serena Wang · Blake Woodworth · Seungil You
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #204
Optimality Implies Kernel Sum Classifiers are Statistically Efficient
In Posters Tue
Raphael Meyer · Jean Honorio
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #205
The Implicit Fairness Criterion of Unconstrained Learning
In Posters Tue
Lydia T. Liu · Max Simchowitz · Moritz Hardt
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #206
Weak Detection of Signal in the Spiked Wigner Model
In Posters Tue
Hye Won Chung · Ji Oon Lee
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #207
Rademacher Complexity for Adversarially Robust Generalization
In Posters Tue
Dong Yin · Kannan Ramchandran · Peter Bartlett
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #208
Provably efficient RL with Rich Observations via Latent State Decoding
In Posters Tue
Simon Du · Akshay Krishnamurthy · Nan Jiang · Alekh Agarwal · Miroslav Dudik · John Langford
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #209
Information-Theoretic Considerations in Batch Reinforcement Learning
In Posters Tue
Jinglin Chen · Nan Jiang
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #210
A Contrastive Divergence for Combining Variational Inference and MCMC
In Posters Tue
Francisco Ruiz · Michalis Titsias
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #211
Calibrated Approximate Bayesian Inference
In Posters Tue
Hanwen Xing · Geoff Nicholls · Jeong Lee
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #212
Moment-Based Variational Inference for Markov Jump Processes
In Posters Tue
Christian Wildner · Heinz Koeppl
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #213
Understanding MCMC Dynamics as Flows on the Wasserstein Space
In Posters Tue
Chang Liu · Jingwei Zhuo · Jun Zhu
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #214
LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations
In Posters Tue
Brian Trippe · Jonathan Huggins · Raj Agrawal · Tamara Broderick
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #215
Amortized Monte Carlo Integration
In Posters Tue
Adam Golinski · Frank Wood · Tom Rainforth
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #216
Stein Point Markov Chain Monte Carlo
In Posters Tue
Wilson Ye Chen · Alessandro Barp · Francois-Xavier Briol · Jackson Gorham · Mark Girolami · Lester Mackey · Chris Oates
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #217
Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations
In Posters Tue
Wu Lin · Mohammad Emtiyaz Khan · Mark Schmidt
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #218
Particle Flow Bayes' Rule
In Posters Tue
Xinshi Chen · Hanjun Dai · Le Song
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #219
Correlated Variational Auto-Encoders
In Posters Tue
Da Tang · Dawen Liang · Tony Jebara · Nicholas Ruozzi
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #220
Towards a Unified Analysis of Random Fourier Features
In Posters Tue
Zhu Li · Jean-Francois Ton · Dino Oglic · Dino Sejdinovic
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #221
Learning deep kernels for exponential family densities
In Posters Tue
Li Kevin Wenliang · D.J. Sutherland · Heiko Strathmann · Arthur Gretton
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #222
Bayesian Deconditional Kernel Mean Embeddings
In Posters Tue
Kelvin Hsu · Fabio Ramos
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #223
A Kernel Perspective for Regularizing Deep Neural Networks
In Posters Tue
Alberto Bietti · Gregoire Mialon · Dexiong Chen · Julien Mairal
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #224
A Persistent Weisfeiler--Lehman Procedure for Graph Classification
In Posters Tue
Bastian Rieck · Christian Bock · Karsten Borgwardt
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #225
Rehashing Kernel Evaluation in High Dimensions
In Posters Tue
Paris Siminelakis · Kexin Rong · Peter Bailis · Moses Charikar · Philip Levis
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #226
Large-Scale Sparse Kernel Canonical Correlation Analysis
In Posters Tue
Viivi Uurtio · Sahely Bhadra · Juho Rousu
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #227
A Kernel Theory of Modern Data Augmentation
In Posters Tue
Tri Dao · Albert Gu · Alexander J Ratner · Virginia Smith · Christopher De Sa · Christopher Re
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #228
kernelPSI: a Post-Selection Inference Framework for Nonlinear Variable Selection
In Posters Tue
Lotfi Slim · Clément Chatelain · Chloe-Agathe Azencott · Jean-Philippe Vert
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #229
Scalable Learning in Reproducing Kernel Krein Spaces
In Posters Tue
Dino Oglic · Thomas Gaertner
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #230
Dirichlet Simplex Nest and Geometric Inference
In Posters Tue
Mikhail Yurochkin · Aritra Guha · Yuekai Sun · XuanLong Nguyen
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #231
Bayesian leave-one-out cross-validation for large data
In Posters Tue
Måns Magnusson · Michael Andersen · Johan Jonasson · Aki Vehtari
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #232
Rao-Blackwellized Stochastic Gradients for Discrete Distributions
In Posters Tue
Runjing Liu · Jeffrey Regier · Nilesh Tripuraneni · Michael Jordan · Jon McAuliffe
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #233
Neurally-Guided Structure Inference
In Posters Tue
Sidi Lu · Jiayuan Mao · Josh Tenenbaum · Jiajun Wu
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #234
Bayesian Joint Spike-and-Slab Graphical Lasso
In Posters Tue
Zehang Li · Tyler Mccormick · Samuel Clark
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #235
Rotation Invariant Householder Parameterization for Bayesian PCA
In Posters Tue
Rajbir-Singh Nirwan · Nils Bertschinger
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #236
A Framework for Bayesian Optimization in Embedded Subspaces
In Posters Tue
Amin Nayebi · Alexander Munteanu · Matthias Poloczek
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #237
Convolutional Poisson Gamma Belief Network
In Posters Tue
CHAOJIE WANG · Bo Chen · SUCHENG XIAO · Mingyuan Zhou
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #238
Automatic Posterior Transformation for Likelihood-Free Inference
In Posters Tue
David Greenberg · Marcel Nonnenmacher · Jakob Macke
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #239
Active Learning for Decision-Making from Imbalanced Observational Data
In Posters Tue
Iiris Sundin · Peter Schulam · Eero Siivola · Aki Vehtari · Suchi Saria · Samuel Kaski
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #240
Validating Causal Inference Models via Influence Functions
In Posters Tue
Ahmed Alaa · Mihaela van der Schaar
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #241
Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks
In Posters Tue
Charith Mendis · Alex Renda · Dr.Saman Amarasinghe · Michael Carbin
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #242
Learning to Groove with Inverse Sequence Transformations
In Posters Tue
Jon Gillick · Adam Roberts · Jesse Engel · Douglas Eck · David Bamman
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #243
Grid-Wise Control for Multi-Agent Reinforcement Learning in Video Game AI
In Posters Tue
Lei Han · Peng Sun · Yali Du · Jiechao Xiong · Qing Wang · Xinghai Sun · Han Liu · Tong Zhang
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #244
HOList: An Environment for Machine Learning of Higher Order Logic Theorem Proving
In Posters Tue
Kshitij Bansal · Sarah Loos · Markus Rabe · Christian Szegedy · Stewart Wilcox
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #245
Molecular Hypergraph Grammar with Its Application to Molecular Optimization
In Posters Tue
Hiroshi Kajino
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #246
Graph Neural Network for Music Score Data and Modeling Expressive Piano Performance
In Posters Tue
Dasaem Jeong · Taegyun Kwon · Yoojin Kim · Juhan Nam
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom
Learning to Prove Theorems via Interacting with Proof Assistants
In Posters Tue
Kaiyu Yang · Jia Deng
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #248
Circuit-GNN: Graph Neural Networks for Distributed Circuit Design
In Posters Tue
GUO ZHANG · Hao He · Dina Katabi
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #249
Learning to Optimize Multigrid PDE Solvers
In Posters Tue
Daniel Greenfeld · Meirav Galun · Ronen Basri · Irad Yavneh · Ron Kimmel
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #250
A Block Coordinate Descent Proximal Method for Simultaneous Filtering and Parameter Estimation
In Posters Tue
Ramin Raziperchikolaei · Harish Bhat
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #251
Learning Hawkes Processes Under Synchronization Noise
In Posters Tue
William Trouleau · Jalal Etesami · Matthias Grossglauser · Negar Kiyavash · Patrick Thiran
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #252
Generative Adversarial User Model for Reinforcement Learning Based Recommendation System
In Posters Tue
Xinshi Chen · Shuang Li · Hui Li · Shaohua Jiang · Yuan Qi · Le Song
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #253
A Statistical Investigation of Long Memory in Language and Music
In Posters Tue
Alexander Greaves-Tunnell · Zaid Harchaoui
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #254
Deep Factors for Forecasting
In Posters Tue
Yuyang Wang · Alex Smola · Danielle Robinson · Jan Gasthaus · Dean Foster · Tim Januschowski
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #255
Weakly-Supervised Temporal Localization via Occurrence Count Learning
In Posters Tue
Julien Schroeter · Kirill Sidorov · David Marshall
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #256
Switching Linear Dynamics for Variational Bayes Filtering
In Posters Tue
Philip Becker-Ehmck · Jan Peters · Patrick van der Smagt
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #257
Imputing Missing Events in Continuous-Time Event Streams
In Posters Tue
Hongyuan Mei · Guanghui Qin · Jason Eisner
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #258
Understanding and Controlling Memory in Recurrent Neural Networks
In Posters Tue
Doron Haviv · Alexander Rivkind · Omri Barak
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #259
Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces
In Posters Tue
Philipp Becker · Harit Pandya · Gregor Gebhardt · Cheng Zhao · C. James Taylor · Gerhard Neumann
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #260
Subspace Robust Wasserstein Distances
In Posters Tue
François-Pierre Paty · Marco Cuturi
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #261
Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models
In Posters Tue
Kaspar Märtens · Kieran Campbell · Christopher Yau
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #262
Active Manifolds: A non-linear analogue to Active Subspaces
In Posters Tue
Robert Bridges · Anthony Gruber · Christopher Felder · Miki Verma · Chelsey Hoff
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #263
Optimal Minimal Margin Maximization with Boosting
In Posters Tue
Alexander Mathiasen · Kasper Green Larsen · Allan Grønlund
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #264
Generalized Linear Rule Models
In Posters Tue
Dennis Wei · Sanjeeb Dash · Tian Gao · Oktay Gunluk
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #265
Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and Applications
In Posters Tue
Pin-Yu Chen · Lingfei Wu · Sijia Liu · Indika Rajapakse
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #266
Variational Inference for sparse network reconstruction from count data
In Posters Tue
Julien Chiquet · Stephane Robin · Mahendra Mariadassou
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #267
Simplifying Graph Convolutional Networks
In Posters Tue
Felix Wu · Amauri Souza · Tianyi Zhang · Christopher Fifty · Tao Yu · Kilian Weinberger
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #268
Robust Influence Maximization for Hyperparametric Models
In Posters Tue
Dimitrios Kalimeris · Gal Kaplun · Yaron Singer
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #269
HyperGAN: A Generative Model for Diverse, Performant Neural Networks
In Posters Tue
Neale Ratzlaff · Fuxin Li
Poster
Tue Jun 11 06:30 PM -- 06:50 PM (PDT) @ Pacific Ballroom #270
Rates of Convergence for Sparse Variational Gaussian Process Regression
In Posters Tue
David Burt · Carl E Rasmussen · Mark van der Wilk
Poster
Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #271
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
In Posters Tue
Francesco Locatello · Stefan Bauer · Mario Lucic · Gunnar Ratsch · Sylvain Gelly · Bernhard Schölkopf · Olivier Bachem