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Talk
Wed Jul 11th 08:45 -- 09:00 AM @ A1
Opening Remarks
Invited Talk
Wed Jul 11th 09:00 -- 10:00 AM @ A1
AI and Security: Lessons, Challenges and Future Directions
Dawn Song
Session
Wed Jul 11th 10:00 -- 10:20 AM @ A1
Best Paper Session 1
Break
Wed Jul 11th 10:30 -- 11:00 AM @ Hall B
Coffee Break
Oral
Wed Jul 11th 11:00 -- 11:20 AM @ Victoria
DiCE: The Infinitely Differentiable Monte Carlo Estimator
Jakob Foerster · Gregory Farquhar · Maruan Al-Shedivat · Tim Rocktäschel · Eric Xing · Shimon Whiteson
Oral
Wed Jul 11th 11:00 -- 11:20 AM @ A3
Transfer Learning via Learning to Transfer
Ying WEI · Yu Zhang · Junzhou Huang · Qiang Yang
Oral
Wed Jul 11th 11:00 -- 11:20 AM @ A9
Optimal Tuning for Divide-and-conquer Kernel Ridge Regression with Massive Data
Ganggang Xu · Zuofeng Shang · Guang Cheng
Oral
Wed Jul 11th 11:00 -- 11:20 AM @ A1
Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs
Andrea Zanette · Emma Brunskill
Oral
Wed Jul 11th 11:00 -- 11:20 AM @ K1+K2
Learning to Explain: An Information-Theoretic Perspective on Model Interpretation
Jianbo Chen · Le Song · Martin Wainwright · Michael Jordan
Oral
Wed Jul 11th 11:00 -- 11:20 AM @ A4
Crowdsourcing with Arbitrary Adversaries
Matthäus Kleindessner · Pranjal Awasthi
Oral
Wed Jul 11th 11:00 -- 11:20 AM @ A7
Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry
Maximillian Nickel · Douwe Kiela
Oral
Wed Jul 11th 11:00 -- 11:20 AM @ A5
Predict and Constrain: Modeling Cardinality in Deep Structured Prediction
Nataly Brukhim · Amir Globerson
Oral
Wed Jul 11th 11:00 -- 11:20 AM @ A6
Nonparametric Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information
Yichong Xu · Hariank Muthakana · Sivaraman Balakrishnan · Aarti Singh · Artur Dubrawski
Oral
Wed Jul 11th 11:00 -- 11:20 AM @ K11
Equivalence of Multicategory SVM and Simplex Cone SVM: Fast Computations and Statistical Theory
Guillaume Pouliot
Oral
Wed Jul 11th 11:20 -- 11:40 AM @ A5
SparseMAP: Differentiable Sparse Structured Inference
Vlad Niculae · Andre Filipe Torres Martins · Mathieu Blondel · Claire Cardie
Oral
Wed Jul 11th 11:20 -- 11:30 AM @ A1
Learning with Abandonment
Sven Schmit · Ramesh Johari
Oral
Wed Jul 11th 11:20 -- 11:30 AM @ A7
Hyperbolic Entailment Cones for Learning Hierarchical Embeddings
Octavian-Eugen Ganea · Gary Becigneul · Thomas Hofmann
Oral
Wed Jul 11th 11:20 -- 11:30 AM @ K11
Quickshift++: Provably Good Initializations for Sample-Based Mean Shift
Heinrich Jiang · Jennifer Jang · Samory Kpotufe
Oral
Wed Jul 11th 11:20 -- 11:30 AM @ Victoria
Learning to search with MCTSnets
Arthur Guez · Theophane Weber · Ioannis Antonoglou · Karen Simonyan · Oriol Vinyals · Daan Wierstra · Remi Munos · David Silver
Oral
Wed Jul 11th 11:20 -- 11:30 AM @ K1+K2
Nonoverlap-Promoting Variable Selection
Pengtao Xie · Hongbao Zhang · Yichen Zhu · Eric Xing
Oral
Wed Jul 11th 11:20 -- 11:40 AM @ A3
Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory
Ron Amit · Ron Meir
Oral
Wed Jul 11th 11:20 -- 11:30 AM @ A4
Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model
Hideaki Imamura · Issei Sato · Masashi Sugiyama
Oral
Wed Jul 11th 11:20 -- 11:30 AM @ A6
Do Outliers Ruin Collaboration?
Mingda Qiao
Oral
Wed Jul 11th 11:20 -- 11:30 AM @ A9
Distributed Nonparametric Regression under Communication Constraints
Yuancheng Zhu · John Lafferty
Oral
Wed Jul 11th 11:30 -- 11:40 AM @ A9
Coded Sparse Matrix Multiplication
Sinong Wang · Jiashang Liu · Ness Shroff
Oral
Wed Jul 11th 11:30 -- 11:40 AM @ K11
Hierarchical Clustering with Structural Constraints
Evangelos Chatziafratis · Rad Niazadeh · Moses Charikar
Oral
Wed Jul 11th 11:30 -- 11:40 AM @ A6
LeapsAndBounds: A Method for Approximately Optimal Algorithm Configuration
Gellért Weisz · Andras Gyorgy · Csaba Szepesvari
Oral
Wed Jul 11th 11:30 -- 11:40 AM @ A1
Lipschitz Continuity in Model-based Reinforcement Learning
Kavosh Asadi · Dipendra Misra · Michael L. Littman
Oral
Wed Jul 11th 11:30 -- 11:40 AM @ A4
Conditional Noise-Contrastive Estimation of Unnormalised Models
Ciwan Ceylan · Michael Gutmann
Oral
Wed Jul 11th 11:30 -- 11:40 AM @ A7
Tree Edit Distance Learning via Adaptive Symbol Embeddings
Benjamin Paaßen · Claudio Gallicchio · Alessio Micheli · CITEC Barbara Hammer
Oral
Wed Jul 11th 11:30 -- 11:40 AM @ Victoria
Differentiable plasticity: training plastic neural networks with backpropagation
Thomas Miconi · Kenneth Stanley · Jeff Clune
Oral
Wed Jul 11th 11:30 -- 11:40 AM @ K1+K2
MSplit LBI: Realizing Feature Selection and Dense Estimation Simultaneously in Few-shot and Zero-shot Learning
Bo Zhao · Xinwei Sun · Yanwei Fu · Yuan Yao · Yizhou Wang
Oral
Wed Jul 11th 11:40 -- 11:50 AM @ A5
Efficient and Consistent Adversarial Bipartite Matching
Rizal Fathony · Sima Behpour · Xinhua Zhang · Brian Ziebart
Oral
Wed Jul 11th 11:40 -- 11:50 AM @ K11
K-means clustering using random matrix sparsification
Kaushik Sinha
Oral
Wed Jul 11th 11:40 -- 11:50 AM @ A3
Bilevel Programming for Hyperparameter Optimization and Meta-Learning
Luca Franceschi · Paolo Frasconi · Saverio Salzo · Riccardo Grazzi · Massimiliano Pontil
Oral
Wed Jul 11th 11:40 -- 11:50 AM @ A9
Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication
Zebang Shen · Aryan Mokhtari · Tengfei Zhou · Peilin Zhao · Hui Qian
Oral
Wed Jul 11th 11:40 -- 11:50 AM @ A6
Variational Network Inference: Strong and Stable with Concrete Support
Amir Dezfouli · Edwin Bonilla · Richard Nock
Oral
Wed Jul 11th 11:40 -- 11:50 AM @ A7
Learning K-way D-dimensional Discrete Codes for Compact Embedding Representations
Ting Chen · Martin Min · Yizhou Sun
Oral
Wed Jul 11th 11:40 -- 11:50 AM @ A4
Deep One-Class Classification
Lukas Ruff · Nico Görnitz · Lucas Deecke · Shoaib Ahmed Siddiqui · Robert Vandermeulen · Alexander Binder · Emmanuel Müller · Marius Kloft
Oral
Wed Jul 11th 11:40 -- 11:50 AM @ A1
Implicit Quantile Networks for Distributional Reinforcement Learning
Will Dabney · Georg Ostrovski · David Silver · Remi Munos
Oral
Wed Jul 11th 11:40 -- 11:50 AM @ Victoria
TACO: Learning Task Decomposition via Temporal Alignment for Control
Kyriacos Shiarlis · Markus Wulfmeier · Sasha Salter · Shimon Whiteson · Herbert Ingmar Posner
Oral
Wed Jul 11th 11:40 -- 11:50 AM @ K1+K2
Black Box FDR
Wesley Tansey · Yixin Wang · David Blei · Raul Rabadan
Oral
Wed Jul 11th 11:50 AM -- 12:00 PM @ A6
Network Global Testing by Counting Graphlets
Jiashun Jin · Zheng Ke · Shengming Luo
Oral
Wed Jul 11th 11:50 AM -- 12:00 PM @ A3
Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing—and Back
Elliot Meyerson · Risto Miikkulainen
Oral
Wed Jul 11th 11:50 AM -- 12:00 PM @ A5
Learning to Speed Up Structured Output Prediction
Xingyuan Pan · Vivek Srikumar
Oral
Wed Jul 11th 11:50 AM -- 12:00 PM @ A4
Deep Density Destructors
David Inouye · Pradeep Ravikumar
Oral
Wed Jul 11th 11:50 AM -- 12:00 PM @ A7
CoVeR: Learning Covariate-Specific Vector Representations with Tensor Decompositions
Kevin Tian · Teng Zhang · James Zou
Oral
Wed Jul 11th 11:50 AM -- 12:00 PM @ A1
More Robust Doubly Robust Off-policy Evaluation
Mehrdad Farajtabar · Yinlam Chow · Mohammad Ghavamzadeh
Oral
Wed Jul 11th 11:50 AM -- 12:00 PM @ K1+K2
Variable Selection via Penalized Neural Network: a Drop-Out-One Loss Approach
Mao Ye · Yan Sun
Oral
Wed Jul 11th 11:50 AM -- 12:00 PM @ K11
Clustering Semi-Random Mixtures of Gaussians
Aravindan Vijayaraghavan · Pranjal Awasthi
Oral
Wed Jul 11th 11:50 AM -- 12:00 PM @ A9
Faster Derivative-Free Stochastic Algorithm for Shared Memory Machines
Bin Gu · Zhouyuan Huo · Cheng Deng · Heng Huang
Oral
Wed Jul 11th 11:50 AM -- 12:00 PM @ Victoria
Graph Networks as Learnable Physics Engines for Inference and Control
Alvaro Sanchez · Nicolas Heess · Jost Springenberg · Josh Merel · Martin Riedmiller · Raia Hadsell · Peter Battaglia
Break
Wed Jul 11th 12:00 -- 01:30 PM @
Lunch - on your own
Oral
Wed Jul 11th 01:30 -- 01:50 PM @ K11
Leveraging Well-Conditioned Bases: Streaming and Distributed Summaries in Minkowski $p$-Norms
Charlie Dickens · Graham Cormode · David Woodruff
Oral
Wed Jul 11th 01:30 -- 01:50 PM @ A5
WHInter: A Working set algorithm for High-dimensional sparse second order Interaction models
Marine LE MORVAN · Jean-Philippe Vert
Oral
Wed Jul 11th 01:30 -- 01:50 PM @ K1+K2
Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design
Ahmed M. Alaa Ibrahim · M van der Schaar
Oral
Wed Jul 11th 01:30 -- 01:50 PM @ Victoria
RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks
Jinsung Yoon · James Jordon · Mihaela van der Schaar
Oral
Wed Jul 11th 01:30 -- 01:50 PM @ A3
Stagewise Safe Bayesian Optimization with Gaussian Processes
Yanan Sui · Vincent Zhuang · Joel Burdick · Yisong Yue
Oral
Wed Jul 11th 01:30 -- 01:50 PM @ A1
Coordinated Exploration in Concurrent Reinforcement Learning
Maria Dimakopoulou · Benjamin Van Roy
Oral
Wed Jul 11th 01:30 -- 01:50 PM @ A4
Markov Modulated Gaussian Cox Processes for Semi-Stationary Intensity Modeling of Events Data
Minyoung Kim
Oral
Wed Jul 11th 01:30 -- 01:50 PM @ A9
Asynchronous Decentralized Parallel Stochastic Gradient Descent
Xiangru Lian · Wei Zhang · Ce Zhang · Ji Liu
Oral
Wed Jul 11th 01:30 -- 01:50 PM @ A7
Improving Optimization in Models With Continuous Symmetry Breaking
Robert Bamler · Stephan Mandt
Oral
Wed Jul 11th 01:30 -- 01:50 PM @ A6
Data-Dependent Stability of Stochastic Gradient Descent
Ilja Kuzborskij · Christoph Lampert
Oral
Wed Jul 11th 01:50 -- 02:10 PM @ A7
A probabilistic framework for multi-view feature learning with many-to-many associations via neural networks
Akifumi Okuno · Tetsuya Hada · Hidetoshi Shimodaira
Oral
Wed Jul 11th 01:50 -- 02:00 PM @ A1
Structured Evolution with Compact Architectures for Scalable Policy Optimization
Krzysztof Choromanski · Mark Rowland · Vikas Sindhwani · Richard E Turner · Adrian Weller
Oral
Wed Jul 11th 01:50 -- 02:10 PM @ A9
signSGD: Compressed Optimisation for Non-Convex Problems
Jeremy Bernstein · Yu-Xiang Wang · Kamyar Azizzadenesheli · Anima Anandkumar
Oral
Wed Jul 11th 01:50 -- 02:10 PM @ K1+K2
Variance Regularized Counterfactual Risk Minimization via Variational Divergence Minimization
Hang Wu · May Wang
Oral
Wed Jul 11th 01:50 -- 02:10 PM @ Victoria
Semi-Supervised Learning via Compact Latent Space Clustering
Konstantinos Kamnitsas · Daniel C. Castro · Loic Le Folgoc · Ian Walker · Ryutaro Tanno · Daniel Rueckert · Ben Glocker · Antonio Criminisi · Aditya Nori
Oral
Wed Jul 11th 01:50 -- 02:10 PM @ A5
Nearly Optimal Robust Subspace Tracking
Praneeth Narayanamurthy · Namrata Vaswani
Oral
Wed Jul 11th 01:50 -- 02:10 PM @ A6
Stability and Generalization of Learning Algorithms that Converge to Global Optima
Zachary Charles · Dimitris Papailiopoulos
Oral
Wed Jul 11th 01:50 -- 02:10 PM @ A3
BOCK : Bayesian Optimization with Cylindrical Kernels
ChangYong Oh · Efstratios Gavves · Max Welling
Oral
Wed Jul 11th 01:50 -- 02:10 PM @ K11
Subspace Embedding and Linear Regression with Orlicz Norm
Alexandr Andoni · Chengyu Lin · Ying Sheng · Peilin Zhong · Ruiqi Zhong
Oral
Wed Jul 11th 01:50 -- 02:10 PM @ A4
Bayesian Quadrature for Multiple Related Integrals
Xiaoyue Xi · Francois-Xavier Briol · Mark Girolami
Oral
Wed Jul 11th 02:00 -- 02:10 PM @ A1
Spotlight: Optimizing Device Placement for Training Deep Neural Networks
Yuanxiang Gao · Department of Electrical and Computer Li Chen · Baochun Li
Oral
Wed Jul 11th 02:10 -- 02:20 PM @ K1+K2
An Estimation and Analysis Framework for the Rasch Model
Andrew Lan · Mung Chiang · Christoph Studer
Oral
Wed Jul 11th 02:10 -- 02:20 PM @ Victoria
Conditional Neural Processes
Marta Garnelo · Dan Rosenbaum · Chris Maddison · Tiago Ramalho · David Saxton · Murray Shanahan · Yee Teh · Danilo J. Rezende · S. M. Ali Eslami
Oral
Wed Jul 11th 02:10 -- 02:20 PM @ A9
Katyusha X: Simple Momentum Method for Stochastic Sum-of-Nonconvex Optimization
Zeyuan Allen-Zhu
Oral
Wed Jul 11th 02:10 -- 02:20 PM @ A1
Gated Path Planning Networks
Lisa Lee · Emilio Parisotto · Devendra Singh Chaplot · Eric Xing · Ruslan Salakhutdinov
Oral
Wed Jul 11th 02:10 -- 02:20 PM @ A7
Learning Steady-States of Iterative Algorithms over Graphs
Hanjun Dai · Zornitsa Kozareva · Bo Dai · Alex Smola · Le Song
Oral
Wed Jul 11th 02:10 -- 02:20 PM @ K11
Stochastic PCA with $\ell_2$ and $\ell_1$ Regularization
Poorya Mianjy · Raman Arora
Oral
Wed Jul 11th 02:10 -- 02:20 PM @ A4
Differentiable Compositional Kernel Learning for Gaussian Processes
Shengyang Sun · Guodong Zhang · Chaoqi Wang · Wenyuan Zeng · Jiaman Li · Roger Grosse
Oral
Wed Jul 11th 02:10 -- 02:20 PM @ A3
BOHB: Robust and Efficient Hyperparameter Optimization at Scale
Stefan Falkner · Aaron Klein · Frank Hutter
Oral
Wed Jul 11th 02:10 -- 02:20 PM @ A6
Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces
Junhong Lin · Volkan Cevher
Oral
Wed Jul 11th 02:10 -- 02:20 PM @ A5
Safe Element Screening for Submodular Function Minimization
Weizhong Zhang · Bin Hong · Lin Ma · Wei Liu · Tong Zhang
Oral
Wed Jul 11th 02:20 -- 02:30 PM @ A9
$D^2$: Decentralized Training over Decentralized Data
Hanlin Tang · Xiangru Lian · Ming Yan · Ce Zhang · Ji Liu
Oral
Wed Jul 11th 02:20 -- 02:30 PM @ Victoria
A Semantic Loss Function for Deep Learning with Symbolic Knowledge
Jingyi Xu · Zilu Zhang · Tal Friedman · Yitao Liang · Guy Van den Broeck
Oral
Wed Jul 11th 02:20 -- 02:30 PM @ A5
Online Convolutional Sparse Coding with Sample-Dependent Dictionary
Yaqing WANG · Quanming Yao · James Kwok · Lionel NI
Oral
Wed Jul 11th 02:20 -- 02:30 PM @ A4
Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression
Haitao Liu · Jianfei Cai · Yi Wang · Yew Soon ONG
Oral
Wed Jul 11th 02:20 -- 02:30 PM @ A6
Dropout Training, Data-dependent Regularization, and Generalization Bounds
Wenlong Mou · Yuchen Zhou · Jun Gao · Liwei Wang
Oral
Wed Jul 11th 02:20 -- 02:30 PM @ A7
Anonymous Walk Embeddings
Sergey Ivanov · Evgeny Burnaev
Oral
Wed Jul 11th 02:20 -- 02:30 PM @ K11
Streaming Principal Component Analysis in Noisy Setting
Teodor Vanislavov Marinov · Poorya Mianjy · Raman Arora
Oral
Wed Jul 11th 02:20 -- 02:30 PM @ K1+K2
End-to-end Active Object Tracking via Reinforcement Learning
Wenhan Luo · Peng Sun · Fangwei Zhong · Wei Liu · Tong Zhang · Yizhou Wang
Oral
Wed Jul 11th 02:20 -- 02:30 PM @ A1
Best Arm Identification in Linear Bandits with Linear Dimension Dependency
Chao Tao · Saúl A. Blanco · Yuan Zhou
Oral
Wed Jul 11th 02:20 -- 02:30 PM @ A3
Bayesian Optimization of Combinatorial Structures
Ricardo Baptista · Matthias Poloczek
Oral
Wed Jul 11th 02:30 -- 02:50 PM @ K1+K2
Deep Predictive Coding Network for Object Recognition
Haiguang Wen · Kuan Han · Junxing Shi · Yizhen Zhang · Eugenio Culurciello · Zhongming Liu
Oral
Wed Jul 11th 02:30 -- 02:50 PM @ A7
Generative Temporal Models with Spatial Memory for Partially Observed Environments
Marco Fraccaro · Danilo J. Rezende · Yori Zwols · Alexander Pritzel · S. M. Ali Eslami · Fabio Viola
Oral
Wed Jul 11th 02:30 -- 02:50 PM @ A5
The Limits of Maxing, Ranking, and Preference Learning
Moein Falahatgar · Ayush Jain · Alon Orlitsky · Venkatadheeraj Pichapati · Vaishakh Ravindrakumar
Oral
Wed Jul 11th 02:30 -- 02:50 PM @ A1
Structured Control Nets for Deep Reinforcement Learning
Mario Srouji · Jian Zhang · Ruslan Salakhutdinov
Oral
Wed Jul 11th 02:30 -- 02:50 PM @ K11
Linear Spectral Estimators and an Application to Phase Retrieval
Ramina Ghods · Andrew Lan · Tom Goldstein · Christoph Studer
Oral
Wed Jul 11th 02:30 -- 02:50 PM @ Victoria
Non-linear motor control by local learning in spiking neural networks
Aditya Gilra · Wulfram Gerstner
Oral
Wed Jul 11th 02:30 -- 02:50 PM @ A3
Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches
Simon Olofsson · Marc P Deisenroth · Ruth Misener
Oral
Wed Jul 11th 02:30 -- 02:50 PM @ A6
Information Theoretic Guarantees for Empirical Risk Minimization with Applications to Model Selection and Large-Scale Optimization
Ibrahim Alabdulmohsin
Oral
Wed Jul 11th 02:30 -- 02:50 PM @ A4
Variational Inference and Model Selection with Generalized Evidence Bounds
Liqun Chen · Chenyang Tao · RUIYI ZHANG · Ricardo Henao · Lawrence Carin
Oral
Wed Jul 11th 02:30 -- 02:50 PM @ A9
An Alternative View: When Does SGD Escape Local Minima?
Bobby Kleinberg · Yuanzhi Li · Yang Yuan
Oral
Wed Jul 11th 02:50 -- 03:00 PM @ A6
The Generalization Error of Dictionary Learning with Moreau Envelopes
ALEXANDROS GEORGOGIANNIS
Oral
Wed Jul 11th 02:50 -- 03:10 PM @ A1
Latent Space Policies for Hierarchical Reinforcement Learning
Tuomas Haarnoja · Kristian Hartikainen · Pieter Abbeel · Sergey Levine
Oral
Wed Jul 11th 02:50 -- 03:00 PM @ Victoria
PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning
Yunbo Wang · Zhifeng Gao · Mingsheng Long · Jianmin Wang · Philip Yu
Oral
Wed Jul 11th 02:50 -- 03:10 PM @ A9
Escaping Saddles with Stochastic Gradients
Hadi Daneshmand · Jonas Kohler · Aurelien Lucchi · Thomas Hofmann
Oral
Wed Jul 11th 02:50 -- 03:00 PM @ A5
Learning a Mixture of Two Multinomial Logits
Flavio Chierichetti · Ravi Kumar · Andrew Tomkins
Oral
Wed Jul 11th 02:50 -- 03:00 PM @ K1+K2
Gradually Updated Neural Networks for Large-Scale Image Recognition
Siyuan Qiao · Zhishuai Zhang · Wei Shen · Bo Wang · Alan Yuille
Oral
Wed Jul 11th 02:50 -- 03:10 PM @ A3
Selecting Representative Examples for Program Synthesis
Yewen Pu · Zachery Miranda · Armando Solar-Lezama · Leslie Kaelbling
Oral
Wed Jul 11th 02:50 -- 03:00 PM @ A7
Disentangling by Factorising
Hyunjik Kim · Andriy Mnih
Oral
Wed Jul 11th 02:50 -- 03:10 PM @ K11
Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization
Jinghui Chen · Pan Xu · Lingxiao Wang · Jian Ma · Quanquan Gu
Oral
Wed Jul 11th 02:50 -- 03:10 PM @ A4
Fixing a Broken ELBO
Alexander Alemi · Ben Poole · Ian Fischer · Joshua V Dillon · Rif Saurous · Kevin Murphy
Oral
Wed Jul 11th 03:00 -- 03:10 PM @ A5
The Weighted Kendall and High-order Kernels for Permutations
Yunlong Jiao · Jean-Philippe Vert
Oral
Wed Jul 11th 03:00 -- 03:10 PM @ A6
On Learning Sparsely Used Dictionaries from Incomplete Samples
Thanh Nguyen · Akshay Soni · Chinmay Hegde
Oral
Wed Jul 11th 03:00 -- 03:10 PM @ A7
Discovering Interpretable Representations for Both Deep Generative and Discriminative Models
Tameem Adel · Zoubin Ghahramani · Adrian Weller
Oral
Wed Jul 11th 03:00 -- 03:10 PM @ K1+K2
Neural Inverse Rendering for General Reflectance Photometric Stereo
Tatsunori Taniai · Takanori Maehara
Oral
Wed Jul 11th 03:00 -- 03:10 PM @ Victoria
Hierarchical Long-term Video Prediction without Supervision
Nevan Wichers · Ruben Villegas · Dumitru Erhan · Honglak Lee
Oral
Wed Jul 11th 03:10 -- 03:20 PM @ A9
Stochastic Variance-Reduced Cubic Regularized Newton Method
Dongruo Zhou · Pan Xu · Quanquan Gu
Oral
Wed Jul 11th 03:10 -- 03:20 PM @ Victoria
Exploiting the Potential of Standard Convolutional Autoencoders for Image Restoration by Evolutionary Search
Masanori SUGANUMA · Mete Ozay · Takayuki Okatani
Oral
Wed Jul 11th 03:10 -- 03:20 PM @ A1
Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings
John Co-Reyes · Yu Xuan Liu · Abhishek Gupta · Benjamin Eysenbach · Pieter Abbeel · Sergey Levine
Oral
Wed Jul 11th 03:10 -- 03:20 PM @ A4
Tighter Variational Bounds are Not Necessarily Better
Tom Rainforth · Adam Kosiorek · Tuan Anh Le · Chris Maddison · Maximilian Igl · Frank Wood · Yee Whye Teh
Oral
Wed Jul 11th 03:10 -- 03:20 PM @ A5
Parameterized Algorithms for the Matrix Completion Problem
Robert Ganian · DePaul Iyad Kanj · Sebastian Ordyniak · Stefan Szeider
Oral
Wed Jul 11th 03:10 -- 03:20 PM @ A7
Learning Independent Causal Mechanisms
Giambattista Parascandolo · Niki Kilbertus · Mateo Rojas-Carulla · Bernhard Schölkopf
Oral
Wed Jul 11th 03:10 -- 03:20 PM @ A3
On the Relationship between Data Efficiency and Error for Uncertainty Sampling
Stephen Mussmann · Percy Liang
Oral
Wed Jul 11th 03:10 -- 03:20 PM @ K11
Signal and Noise Statistics Oblivious Orthogonal Matching Pursuit
Sreejith Kallummil · Sheetal Kalyani
Oral
Wed Jul 11th 03:10 -- 03:20 PM @ K1+K2
One-Shot Segmentation in Clutter
Claudio Michaelis · Matthias Bethge · Alexander Ecker
Oral
Wed Jul 11th 03:10 -- 03:20 PM @ A6
The Well-Tempered Lasso
Yuanzhi Li · Yoram Singer
Oral
Wed Jul 11th 03:20 -- 03:30 PM @ Victoria
Model-Level Dual Learning
Yingce Xia · Xu Tan · Fei Tian · Tao Qin · Nenghai Yu · Tie-Yan Liu
Oral
Wed Jul 11th 03:20 -- 03:30 PM @ K1+K2
Active Testing: An Efficient and Robust Framework for Estimating Accuracy
Phuc Nguyen · Deva Ramanan · Charless Fowlkes
Oral
Wed Jul 11th 03:20 -- 03:30 PM @ A7
Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing
Davide Bacciu · Federico Errica · Alessio Micheli
Oral
Wed Jul 11th 03:20 -- 03:30 PM @ A1
An Inference-Based Policy Gradient Method for Learning Options
Matthew Smith · Herke van Hoof · Joelle Pineau
Oral
Wed Jul 11th 03:20 -- 03:30 PM @ A6
Differentially Private Identity and Equivalence Testing of Discrete Distributions
Maryam Aliakbarpour · Ilias Diakonikolas · MIT Ronitt Rubinfeld
Oral
Wed Jul 11th 03:20 -- 03:30 PM @ K11
Testing Sparsity over Known and Unknown Bases
Siddharth Barman · Arnab Bhattacharyya · Suprovat Ghoshal
Oral
Wed Jul 11th 03:20 -- 03:30 PM @ A4
Continuous-Time Flows for Efficient Inference and Density Estimation
Changyou Chen · Chunyuan Li · Liquan Chen · Wenlin Wang · Yunchen Pu · Lawrence Carin
Oral
Wed Jul 11th 03:20 -- 03:30 PM @ A9
Non-convex Conditional Gradient Sliding
chao qu · Yan Li · Huan Xu
Break
Wed Jul 11th 03:30 -- 04:00 PM @ Hall B
Coffee Break
Oral
Wed Jul 11th 04:00 -- 04:20 PM @ A6
Differentially Private Matrix Completion Revisited
Prateek Jain · Om Dipakbhai Thakkar · Abhradeep Thakurta
Oral
Wed Jul 11th 04:00 -- 04:20 PM @ A9
Dissipativity Theory for Accelerating Stochastic Variance Reduction: A Unified Analysis of SVRG and Katyusha Using Semidefinite Programs
Bin Hu · Stephen Wright · Laurent Lessard
Oral
Wed Jul 11th 04:00 -- 04:20 PM @ A5
Stochastic Training of Graph Convolutional Networks with Variance Reduction
Jianfei Chen · Jun Zhu · Le Song
Oral
Wed Jul 11th 04:00 -- 04:20 PM @ K1+K2
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks
Lechao Xiao · Yasaman Bahri · Jascha Sohl-Dickstein · Samuel Schoenholz · Jeffrey Pennington
Oral
Wed Jul 11th 04:00 -- 04:20 PM @ A4
Semi-Implicit Variational Inference
Mingzhang Yin · Mingyuan Zhou
Oral
Wed Jul 11th 04:00 -- 04:20 PM @ A7
Which Training Methods for GANs do actually Converge?
Lars Mescheder · Andreas Geiger · Sebastian Nowozin
Oral
Wed Jul 11th 04:00 -- 04:20 PM @ A1
Programmatically Interpretable Reinforcement Learning
Abhinav Verma · Vijayaraghavan Murali · Rishabh Singh · Pushmeet Kohli · Swarat Chaudhuri
Oral
Wed Jul 11th 04:00 -- 04:20 PM @ Victoria
Adafactor: Adaptive Learning Rates with Sublinear Memory Cost
Noam Shazeer · Mitchell Stern
Oral
Wed Jul 11th 04:00 -- 04:20 PM @ K11
Weakly Submodular Maximization Beyond Cardinality Constraints: Does Randomization Help Greedy?
Lin Chen · Moran Feldman · Amin Karbasi
Oral
Wed Jul 11th 04:00 -- 04:20 PM @ A3
Competitive Multi-agent Inverse Reinforcement Learning with Sub-optimal Demonstrations
IEMS Xingyu Wang · Diego Klabjan
Oral
Wed Jul 11th 04:20 -- 04:30 PM @ A6
Mitigating Bias in Adaptive Data Gathering via Differential Privacy
Seth V Neel · Aaron Roth
Oral
Wed Jul 11th 04:20 -- 04:40 PM @ K1+K2
The Dynamics of Learning: A Random Matrix Approach
Zhenyu Liao · Romain Couillet
Oral
Wed Jul 11th 04:20 -- 04:40 PM @ A7
Chi-square Generative Adversarial Network
Chenyang Tao · Liqun Chen · Ricardo Henao · Jianfeng Feng · Lawrence Carin
Oral
Wed Jul 11th 04:20 -- 04:30 PM @ A9
Lyapunov Functions for First-Order Methods: Tight Automated Convergence Guarantees
Adrien Taylor · Bryan Van Scoy · Laurent Lessard
Oral
Wed Jul 11th 04:20 -- 04:40 PM @ A4
Efficient Gradient-Free Variational Inference using Policy Search
Oleg Arenz · Gerhard Neumann · Mingjun Zhong
Oral
Wed Jul 11th 04:20 -- 04:30 PM @ Victoria
Orthogonal Recurrent Neural Networks with Scaled Cayley Transform
Kyle Helfrich · Devin Willmott · Qiang Ye
Oral
Wed Jul 11th 04:20 -- 04:40 PM @ A1
Learning by Playing - Solving Sparse Reward Tasks from Scratch
Martin Riedmiller · Roland Hafner · Thomas Lampe · Michael Neunert · Jonas Degrave · Tom Van de Wiele · Vlad Mnih · Nicolas Heess · Jost Springenberg
Oral
Wed Jul 11th 04:20 -- 04:40 PM @ A3
Feedback-Based Tree Search for Reinforcement Learning
Daniel Jiang · Emmanuel Ekwedike · Han Liu
Oral
Wed Jul 11th 04:20 -- 04:40 PM @ A5
Representation Learning on Graphs with Jumping Knowledge Networks
Keyulu Xu · Chengtao Li · Yonglong Tian · Tomohiro Sonobe · Ken-ichi Kawarabayashi · Stefanie Jegelka
Oral
Wed Jul 11th 04:20 -- 04:40 PM @ K11
Beyond 1/2-Approximation for Submodular Maximization on Massive Data Streams
Ashkan Norouzi-Fard · Jakub Tarnawski · Slobodan Mitrovic · Amir Zandieh · Aidasadat Mousavifar · Ola Svensson
Oral
Wed Jul 11th 04:30 -- 04:40 PM @ Victoria
Kronecker Recurrent Units
Cijo Jose · Mouhamadou Moustapha Cisse · Francois Fleuret
Oral
Wed Jul 11th 04:30 -- 04:40 PM @ A6
Local Private Hypothesis Testing: Chi-Square Tests
Marco Gaboardi · Ryan Rogers
Oral
Wed Jul 11th 04:30 -- 04:40 PM @ A9
ADMM and Accelerated ADMM as Continuous Dynamical Systems
Guilherme Franca · Daniel Robinson · Rene Vidal
Oral
Wed Jul 11th 04:40 -- 04:50 PM @ A6
Locally Private Hypothesis Testing
Or Sheffet
Oral
Wed Jul 11th 04:40 -- 04:50 PM @ A7
Learning Implicit Generative Models with the Method of Learned Moments
Suman Ravuri · Shakir Mohamed · Mihaela Rosca · Oriol Vinyals
Oral
Wed Jul 11th 04:40 -- 04:50 PM @ A4
A Spectral Approach to Gradient Estimation for Implicit Distributions
Jiaxin Shi · Shengyang Sun · Jun Zhu
Oral
Wed Jul 11th 04:40 -- 04:50 PM @ A9
Computational Optimal Transport: Complexity by Accelerated Gradient Descent Is Better Than by Sinkhorn's Algorithm
Pavel Dvurechenskii · Alexander Gasnikov · Alexey Kroshnin
Oral
Wed Jul 11th 04:40 -- 04:50 PM @ A3
Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling
kyowoon Lee · Sol-A Kim · Jaesik Choi · Seong-Whan Lee
Oral
Wed Jul 11th 04:40 -- 04:50 PM @ K11
Scalable Deletion-Robust Submodular Maximization: Data Summarization with Privacy and Fairness Constraints
Ehsan Kazemi · Morteza Zadimoghaddam · Amin Karbasi
Oral
Wed Jul 11th 04:40 -- 04:50 PM @ A1
Automatic Goal Generation for Reinforcement Learning Agents
Carlos Florensa · David Held · Xinyang Geng · Pieter Abbeel
Oral
Wed Jul 11th 04:40 -- 04:50 PM @ Victoria
Fast Parametric Learning with Activation Memorization
Jack Rae · Chris Dyer · Peter Dayan · Timothy Lillicrap
Oral
Wed Jul 11th 04:40 -- 04:50 PM @ K1+K2
On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization
Sanjeev Arora · Nadav Cohen · Elad Hazan
Oral
Wed Jul 11th 04:40 -- 04:50 PM @ A5
Learning Diffusion using Hyperparameters
Dimitrios Kalimeris · Yaron Singer · Karthik Subbian · Udi Weinsberg
Oral
Wed Jul 11th 04:50 -- 05:00 PM @ K1+K2
Deep Linear Networks with Arbitrary Loss: All Local Minima Are Global
Thomas Laurent · James von Brecht
Oral
Wed Jul 11th 04:50 -- 05:00 PM @ A4
Quasi-Monte Carlo Variational Inference
Alexander Buchholz · Florian Wenzel · Stephan Mandt
Oral
Wed Jul 11th 04:50 -- 05:00 PM @ A7
A Classification-Based Study of Covariate Shift in GAN Distributions
Shibani Santurkar · Ludwig Schmidt · Aleksander Madry
Oral
Wed Jul 11th 04:50 -- 05:00 PM @ Victoria
Dynamic Evaluation of Neural Sequence Models
Ben Krause · Emmanuel Kahembwe · Iain Murray · Steve Renals
Oral
Wed Jul 11th 04:50 -- 05:00 PM @ A9
An Efficient Semismooth Newton based Algorithm for Convex Clustering
Yancheng Yuan · Defeng Sun · Kim-Chuan Toh
Oral
Wed Jul 11th 04:50 -- 05:00 PM @ A3
Learning the Reward Function for a Misspecified Model
Erik Talvitie
Oral
Wed Jul 11th 04:50 -- 05:00 PM @ K11
Data Summarization at Scale: A Two-Stage Submodular Approach
Marko Mitrovic · Ehsan Kazemi · Morteza Zadimoghaddam · Amin Karbasi
Oral
Wed Jul 11th 04:50 -- 05:00 PM @ A1
Universal Planning Networks: Learning Generalizable Representations for Visuomotor Control
Aravind Srinivas · Allan Jabri · Pieter Abbeel · Sergey Levine · Chelsea Finn
Oral
Wed Jul 11th 04:50 -- 05:00 PM @ A5
Canonical Tensor Decomposition for Knowledge Base Completion
Timothee Lacroix · Nicolas Usunier · Guillaume R Obozinski
Oral
Wed Jul 11th 04:50 -- 05:00 PM @ A6
INSPECTRE: Privately Estimating the Unseen
Jayadev Acharya · Gautam Kamath · Ziteng Sun · Huanyu Zhang
Oral
Wed Jul 11th 05:00 -- 05:20 PM @ A3
Machine Theory of Mind
Neil Rabinowitz · Frank Perbet · Francis Song · Chiyuan Zhang · S. M. Ali Eslami · Matthew Botvinick
Oral
Wed Jul 11th 05:00 -- 05:20 PM @ K11
Learning to Optimize Combinatorial Functions
Nir Rosenfeld · Eric Balkanski · Amir Globerson · Yaron Singer
Oral
Wed Jul 11th 05:00 -- 05:20 PM @ A4
Yes, but Did It Work?: Evaluating Variational Inference
Yuling Yao · Aki Vehtari · Daniel Simpson · Andrew Gelman
Oral
Wed Jul 11th 05:00 -- 05:20 PM @ A6
Delayed Impact of Fair Machine Learning
Lydia T. Liu · Sarah Dean · Esther Rolf · Max Simchowitz · University of California Moritz Hardt
Oral
Wed Jul 11th 05:00 -- 05:20 PM @ K1+K2
Essentially No Barriers in Neural Network Energy Landscape
Felix Draxler · Kambis Veschgini · Manfred Salmhofer · Fred Hamprecht
Oral
Wed Jul 11th 05:00 -- 05:20 PM @ A7
Differentiable Abstract Interpretation for Provably Robust Neural Networks
Matthew Mirman · Timon Gehr · Martin Vechev
Oral
Wed Jul 11th 05:00 -- 05:20 PM @ A1
Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning
Ronan Fruit · Matteo Pirotta · Alessandro Lazaric · Ronald Ortner
Oral
Wed Jul 11th 05:00 -- 05:20 PM @ A9
The Power of Interpolation: Understanding the Effectiveness of SGD in Modern Over-parametrized Learning
Siyuan Ma · Raef Bassily · Mikhail Belkin
Oral
Wed Jul 11th 05:00 -- 05:20 PM @ Victoria
Decoupled Parallel Backpropagation with Convergence Guarantee
Zhouyuan Huo · Bin Gu · Qian Yang · Heng Huang
Oral
Wed Jul 11th 05:00 -- 05:20 PM @ A5
Dependent Relational Gamma Process Models for Longitudinal Networks
Sikun Yang · Heinz Koeppl
Oral
Wed Jul 11th 05:20 -- 05:40 PM @ Victoria
Efficient Neural Architecture Search via Parameters Sharing
Hieu Pham · Melody Guan · Barret Zoph · Quoc Le · Jeff Dean
Oral
Wed Jul 11th 05:20 -- 05:40 PM @ A7
Provable Defenses against Adversarial Examples via the Convex Outer Adversarial Polytope
Eric Wong · Zico Kolter
Oral
Wed Jul 11th 05:20 -- 05:40 PM @ A6
Fairness Without Demographics in Repeated Loss Minimization
Tatsunori Hashimoto · Megha Srivastava · Hongseok Namkoong · Percy Liang
Oral
Wed Jul 11th 05:20 -- 05:30 PM @ A9
Fast Variance Reduction Method with Stochastic Batch Size
University of California Xuanqing Liu · Cho-Jui Hsieh
Oral
Wed Jul 11th 05:20 -- 05:40 PM @ A1
Path Consistency Learning in Tsallis Entropy Regularized MDPs
Yinlam Chow · Ofir Nachum · Mohammad Ghavamzadeh
Oral
Wed Jul 11th 05:20 -- 05:40 PM @ A5
NetGAN: Generating Graphs via Random Walks
Aleksandar Bojchevski · Oleksandr Shchur · Daniel Zügner · Stephan Günnemann
Oral
Wed Jul 11th 05:20 -- 05:30 PM @ K1+K2
Comparing Dynamics: Deep Neural Networks versus Glassy Systems
Marco Baity-Jesi · Levent Sagun · Mario Geiger · Stefano Spigler · Gerard Arous · Chiara Cammarota · Yann LeCun · Matthieu Wyart · Giulio Biroli
Oral
Wed Jul 11th 05:20 -- 05:40 PM @ A4
Augment and Reduce: Stochastic Inference for Large Categorical Distributions
Francisco Ruiz · Michalis Titsias · Adji Bousso Dieng · David Blei
Oral
Wed Jul 11th 05:20 -- 05:40 PM @ A3
Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement
Andre Barreto · Diana Borsa · John Quan · Tom Schaul · David Silver · Matteo Hessel · Daniel J. Mankowitz · Augustin Zidek · Remi Munos
Oral
Wed Jul 11th 05:20 -- 05:30 PM @ K11
Proportional Allocation: Simple, Distributed, and Diverse Matching with High Entropy
Shipra Agarwal · Morteza Zadimoghaddam · Vahab Mirrokni
Oral
Wed Jul 11th 05:30 -- 05:40 PM @ A9
SGD and Hogwild! Convergence Without the Bounded Gradients Assumption
Lam Nguyen · PHUONG HA NGUYEN · Marten van Dijk · Peter Richtarik · Katya Scheinberg · Martin Takac
Oral
Wed Jul 11th 05:30 -- 05:40 PM @ K1+K2
Not All Samples Are Created Equal: Deep Learning with Importance Sampling
Angelos Katharopoulos · Francois Fleuret
Oral
Wed Jul 11th 05:30 -- 05:40 PM @ K11
Binary Partitions with Approximate Minimum Impurity
Eduardo Laber · Marco Molinaro · Felipe de A. Mello Pereira
Oral
Wed Jul 11th 05:40 -- 05:50 PM @ A3
Been There, Done That: Meta-Learning with Episodic Recall
Samuel Ritter · Jane Wang · Zeb Kurth-Nelson · Siddhant Jayakumar · Charles Blundell · Razvan Pascanu · Matthew Botvinick
Oral
Wed Jul 11th 05:40 -- 05:50 PM @ K1+K2
Learning Deep ResNet Blocks Sequentially using Boosting Theory
Furong Huang · Jordan Ash · John Langford · Robert Schapire
Oral
Wed Jul 11th 05:40 -- 05:50 PM @ A6
Nonconvex Optimization for Regression with Fairness Constraints
Junpei Komiyama · Akiko Takeda · Junya Honda · Hajime Shimao
Oral
Wed Jul 11th 05:40 -- 05:50 PM @ A7
Synthesizing Robust Adversarial Examples
Anish Athalye · Logan Engstrom · Andrew Ilyas · Kevin Kwok
Oral
Wed Jul 11th 05:40 -- 05:50 PM @ A1
Improved Regret Bounds for Thompson Sampling in Linear Quadratic Control Problems
Marc Abeille · Alessandro Lazaric
Oral
Wed Jul 11th 05:40 -- 05:50 PM @ A5
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models
Jiaxuan You · Rex (Zhitao) Ying · Xiang Ren · Will Hamilton · Jure Leskovec
Oral
Wed Jul 11th 05:40 -- 05:50 PM @ A9
Lightweight Stochastic Optimization for Minimizing Finite Sums with Infinite Data
Shuai Zheng · James Kwok
Oral
Wed Jul 11th 05:40 -- 05:50 PM @ A4
Black-Box Variational Inference for Stochastic Differential Equations
Tom Ryder · Andrew Golightly · Stephen McGough · Dennis Prangle
Oral
Wed Jul 11th 05:40 -- 05:50 PM @ Victoria
Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization
Jiong Zhang · Qi Lei · Inderjit Dhillon
Oral
Wed Jul 11th 05:40 -- 05:50 PM @ K11
Partial Optimality and Fast Lower Bounds for Weighted Correlation Clustering
Jan-Hendrik Lange · Andreas Karrenbauer · Bjoern Andres
Oral
Wed Jul 11th 05:50 -- 06:00 PM @ K11
Bounds on the Approximation Power of Feedforward Neural Networks
Mohammad Mehrabi · Aslan Tchamkerten · MANSOOR I YOUSEFI
Oral
Wed Jul 11th 05:50 -- 06:00 PM @ A5
Neural Relational Inference for Interacting Systems
Thomas Kipf · Ethan Fetaya · Kuan-Chieh Wang · Max Welling · Richard Zemel
Oral
Wed Jul 11th 05:50 -- 06:00 PM @ A4
Inference Suboptimality in Variational Autoencoders
Chris Cremer · Xuechen Li · David Duvenaud
Oral
Wed Jul 11th 05:50 -- 06:00 PM @ A7
Adversarial Risk and the Dangers of Evaluating Against Weak Attacks
Jonathan Uesato · Brendan O'Donoghue · Pushmeet Kohli · Aäron van den Oord
Oral
Wed Jul 11th 05:50 -- 06:00 PM @ A9
A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates
Kaiwen Zhou · Fanhua Shang · James Cheng
Oral
Wed Jul 11th 05:50 -- 06:00 PM @ Victoria
Spline Filters For End-to-End Deep Learning
Randall Balestriero · Romain Cosentino · Herve Glotin · Richard Baraniuk
Oral
Wed Jul 11th 05:50 -- 06:00 PM @ K1+K2
An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks
Qianxiao Li · IHPC Shuji Hao
Oral
Wed Jul 11th 05:50 -- 06:00 PM @ A3
Continual Reinforcement Learning with Complex Synapses
Christos Kaplanis · Murray Shanahan · Claudia Clopath
Oral
Wed Jul 11th 05:50 -- 06:00 PM @ A6
Fair and Diverse DPP-Based Data Summarization
Elisa Celis · Vijay Keswani · Damian Straszak · Amit Jayant Deshpande · Tarun Kathuria · Nisheeth Vishnoi
Oral
Wed Jul 11th 05:50 -- 06:00 PM @ A1
Least-Squares Temporal Difference Learning for the Linear Quadratic Regulator
Stephen Tu · Benjamin Recht
Break
Wed Jul 11th 06:15 -- 07:15 PM @ Hall B
Light Evening Snack
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #1
Spline Filters For End-to-End Deep Learning
Randall Balestriero · Romain Cosentino · Herve Glotin · Richard Baraniuk
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #2
Non-linear motor control by local learning in spiking neural networks
Aditya Gilra · Wulfram Gerstner
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #3
Implicit Quantile Networks for Distributional Reinforcement Learning
Will Dabney · Georg Ostrovski · David Silver · Remi Munos
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #4
An Inference-Based Policy Gradient Method for Learning Options
Matthew Smith · Herke van Hoof · Joelle Pineau
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #5
Predict and Constrain: Modeling Cardinality in Deep Structured Prediction
Nataly Brukhim · Amir Globerson
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #6
Differentially Private Matrix Completion Revisited
Prateek Jain · Om Dipakbhai Thakkar · Abhradeep Thakurta
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #7
Differentiable plasticity: training plastic neural networks with backpropagation
Thomas Miconi · Kenneth Stanley · Jeff Clune
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #8
Model-Level Dual Learning
Yingce Xia · Xu Tan · Fei Tian · Tao Qin · Nenghai Yu · Tie-Yan Liu
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #9
CoVeR: Learning Covariate-Specific Vector Representations with Tensor Decompositions
Kevin Tian · Teng Zhang · James Zou
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #10
Tree Edit Distance Learning via Adaptive Symbol Embeddings
Benjamin Paaßen · Claudio Gallicchio · Alessio Micheli · CITEC Barbara Hammer
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #11
Gradually Updated Neural Networks for Large-Scale Image Recognition
Siyuan Qiao · Zhishuai Zhang · Wei Shen · Bo Wang · Alan Yuille
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #12
One-Shot Segmentation in Clutter
Claudio Michaelis · Matthias Bethge · Alexander Ecker
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #13
Active Testing: An Efficient and Robust Framework for Estimating Accuracy
Phuc Nguyen · Deva Ramanan · Charless Fowlkes
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #14
Learning Deep ResNet Blocks Sequentially using Boosting Theory
Furong Huang · Jordan Ash · John Langford · Robert Schapire
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #15
Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings
John Co-Reyes · Yu Xuan Liu · Abhishek Gupta · Benjamin Eysenbach · Pieter Abbeel · Sergey Levine
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #16
Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs
Andrea Zanette · Emma Brunskill
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #17
Stochastic PCA with $\ell_2$ and $\ell_1$ Regularization
Poorya Mianjy · Raman Arora
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #18
Subspace Embedding and Linear Regression with Orlicz Norm
Alexandr Andoni · Chengyu Lin · Ying Sheng · Peilin Zhong · Ruiqi Zhong
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #19
Signal and Noise Statistics Oblivious Orthogonal Matching Pursuit
Sreejith Kallummil · Sheetal Kalyani
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #20
Provable Defenses against Adversarial Examples via the Convex Outer Adversarial Polytope
Eric Wong · Zico Kolter
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #21
Learning the Reward Function for a Misspecified Model
Erik Talvitie
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #22
Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling
kyowoon Lee · Sol-A Kim · Jaesik Choi · Seong-Whan Lee
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #23
Do Outliers Ruin Collaboration?
Mingda Qiao
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #24
Dropout Training, Data-dependent Regularization, and Generalization Bounds
Wenlong Mou · Yuchen Zhou · Jun Gao · Liwei Wang
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #25
Competitive Multi-agent Inverse Reinforcement Learning with Sub-optimal Demonstrations
IEMS Xingyu Wang · Diego Klabjan
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #26
Continual Reinforcement Learning with Complex Synapses
Christos Kaplanis · Murray Shanahan · Claudia Clopath
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #27
Equivalence of Multicategory SVM and Simplex Cone SVM: Fast Computations and Statistical Theory
Guillaume Pouliot
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #28
Quickshift++: Provably Good Initializations for Sample-Based Mean Shift
Heinrich Jiang · Jennifer Jang · Samory Kpotufe
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #29
Learning Diffusion using Hyperparameters
Dimitrios Kalimeris · Yaron Singer · Karthik Subbian · Udi Weinsberg
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #30
Learning a Mixture of Two Multinomial Logits
Flavio Chierichetti · Ravi Kumar · Andrew Tomkins
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #31
Crowdsourcing with Arbitrary Adversaries
Matthäus Kleindessner · Pranjal Awasthi
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #32
Deep Density Destructors
David Inouye · Pradeep Ravikumar
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #33
Programmatically Interpretable Reinforcement Learning
Abhinav Verma · Vijayaraghavan Murali · Rishabh Singh · Pushmeet Kohli · Swarat Chaudhuri
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #34
Structured Evolution with Compact Architectures for Scalable Policy Optimization
Krzysztof Choromanski · Mark Rowland · Vikas Sindhwani · Richard E Turner · Adrian Weller
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #35
The Weighted Kendall and High-order Kernels for Permutations
Yunlong Jiao · Jean-Philippe Vert
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #36
The Limits of Maxing, Ranking, and Preference Learning
Moein Falahatgar · Ayush Jain · Alon Orlitsky · Venkatadheeraj Pichapati · Vaishakh Ravindrakumar
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #37
Black Box FDR
Wesley Tansey · Yixin Wang · David Blei · Raul Rabadan
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #38
Variable Selection via Penalized Neural Network: a Drop-Out-One Loss Approach
Mao Ye · Yan Sun
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #39
Clustering Semi-Random Mixtures of Gaussians
Aravindan Vijayaraghavan · Pranjal Awasthi
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #40
Leveraging Well-Conditioned Bases: Streaming and Distributed Summaries in Minkowski $p$-Norms
Charlie Dickens · Graham Cormode · David Woodruff
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #41
Learning by Playing - Solving Sparse Reward Tasks from Scratch
Martin Riedmiller · Roland Hafner · Thomas Lampe · Michael Neunert · Jonas Degrave · Tom Van de Wiele · Vlad Mnih · Nicolas Heess · Jost Springenberg
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #42
Structured Control Nets for Deep Reinforcement Learning
Mario Srouji · Jian Zhang · Ruslan Salakhutdinov
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #43
Stagewise Safe Bayesian Optimization with Gaussian Processes
Yanan Sui · Vincent Zhuang · Joel Burdick · Yisong Yue
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #44
Bayesian Optimization of Combinatorial Structures
Ricardo Baptista · Matthias Poloczek
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #45
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models
Jiaxuan You · Rex (Zhitao) Ying · Xiang Ren · Will Hamilton · Jure Leskovec
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #46
Dependent Relational Gamma Process Models for Longitudinal Networks
Sikun Yang · Heinz Koeppl
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #47
K-means clustering using random matrix sparsification
Kaushik Sinha
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #48
Hierarchical Clustering with Structural Constraints
Evangelos Chatziafratis · Rad Niazadeh · Moses Charikar
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #49
Kronecker Recurrent Units
Cijo Jose · Mouhamadou Moustapha Cisse · Francois Fleuret
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #50
Semi-Supervised Learning via Compact Latent Space Clustering
Konstantinos Kamnitsas · Daniel C. Castro · Loic Le Folgoc · Ian Walker · Ryutaro Tanno · Daniel Rueckert · Ben Glocker · Antonio Criminisi · Aditya Nori
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #51
Dynamic Evaluation of Neural Sequence Models
Ben Krause · Emmanuel Kahembwe · Iain Murray · Steve Renals
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #52
TACO: Learning Task Decomposition via Temporal Alignment for Control
Kyriacos Shiarlis · Markus Wulfmeier · Sasha Salter · Shimon Whiteson · Herbert Ingmar Posner
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #53
A Spectral Approach to Gradient Estimation for Implicit Distributions
Jiaxin Shi · Shengyang Sun · Jun Zhu
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #54
Quasi-Monte Carlo Variational Inference
Alexander Buchholz · Florian Wenzel · Stephan Mandt
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #55
Learning to Optimize Combinatorial Functions
Nir Rosenfeld · Eric Balkanski · Amir Globerson · Yaron Singer
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #56
Proportional Allocation: Simple, Distributed, and Diverse Matching with High Entropy
Shipra Agarwal · Morteza Zadimoghaddam · Vahab Mirrokni
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #57
Representation Learning on Graphs with Jumping Knowledge Networks
Keyulu Xu · Chengtao Li · Yonglong Tian · Tomohiro Sonobe · Ken-ichi Kawarabayashi · Stefanie Jegelka
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #58
NetGAN: Generating Graphs via Random Walks
Aleksandar Bojchevski · Oleksandr Shchur · Daniel Zügner · Stephan Günnemann
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #59
INSPECTRE: Privately Estimating the Unseen
Jayadev Acharya · Gautam Kamath · Ziteng Sun · Huanyu Zhang
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #60
Locally Private Hypothesis Testing
Or Sheffet
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #61
Latent Space Policies for Hierarchical Reinforcement Learning
Tuomas Haarnoja · Kristian Hartikainen · Pieter Abbeel · Sergey Levine
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #62
More Robust Doubly Robust Off-policy Evaluation
Mehrdad Farajtabar · Yinlam Chow · Mohammad Ghavamzadeh
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #63
Learning to Explain: An Information-Theoretic Perspective on Model Interpretation
Jianbo Chen · Le Song · Martin Wainwright · Michael Jordan
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #64
End-to-end Active Object Tracking via Reinforcement Learning
Wenhan Luo · Peng Sun · Fangwei Zhong · Wei Liu · Tong Zhang · Yizhou Wang
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #65
Efficient and Consistent Adversarial Bipartite Matching
Rizal Fathony · Sima Behpour · Xinhua Zhang · Brian Ziebart
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #66
SparseMAP: Differentiable Sparse Structured Inference
Vlad Niculae · Andre Filipe Torres Martins · Mathieu Blondel · Claire Cardie
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #67
Bilevel Programming for Hyperparameter Optimization and Meta-Learning
Luca Franceschi · Paolo Frasconi · Saverio Salzo · Riccardo Grazzi · Massimiliano Pontil
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #68
Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory
Ron Amit · Ron Meir
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #69
Parameterized Algorithms for the Matrix Completion Problem
Robert Ganian · DePaul Iyad Kanj · Sebastian Ordyniak · Stefan Szeider
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #70
Nearly Optimal Robust Subspace Tracking
Praneeth Narayanamurthy · Namrata Vaswani
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #71
Katyusha X: Simple Momentum Method for Stochastic Sum-of-Nonconvex Optimization
Zeyuan Allen-Zhu
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #72
signSGD: Compressed Optimisation for Non-Convex Problems
Jeremy Bernstein · Yu-Xiang Wang · Kamyar Azizzadenesheli · Anima Anandkumar
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #73
Synthesizing Robust Adversarial Examples
Anish Athalye · Logan Engstrom · Andrew Ilyas · Kevin Kwok
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #74
Differentiable Abstract Interpretation for Provably Robust Neural Networks
Matthew Mirman · Timon Gehr · Martin Vechev
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #75
Stochastic Training of Graph Convolutional Networks with Variance Reduction
Jianfei Chen · Jun Zhu · Le Song
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #76
Neural Relational Inference for Interacting Systems
Thomas Kipf · Ethan Fetaya · Kuan-Chieh Wang · Max Welling · Richard Zemel
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #77
Which Training Methods for GANs do actually Converge?
Lars Mescheder · Andreas Geiger · Sebastian Nowozin
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #78
Learning Independent Causal Mechanisms
Giambattista Parascandolo · Niki Kilbertus · Mateo Rojas-Carulla · Bernhard Schölkopf
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #79
Nonconvex Optimization for Regression with Fairness Constraints
Junpei Komiyama · Akiko Takeda · Junya Honda · Hajime Shimao
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #80
Fairness Without Demographics in Repeated Loss Minimization
Tatsunori Hashimoto · Megha Srivastava · Hongseok Namkoong · Percy Liang
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #81
MSplit LBI: Realizing Feature Selection and Dense Estimation Simultaneously in Few-shot and Zero-shot Learning
Bo Zhao · Xinwei Sun · Yanwei Fu · Yuan Yao · Yizhou Wang
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #82
Nonoverlap-Promoting Variable Selection
Pengtao Xie · Hongbao Zhang · Yichen Zhu · Eric Xing
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #83
Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication
Zebang Shen · Aryan Mokhtari · Tengfei Zhou · Peilin Zhao · Hui Qian
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #84
Graph Networks as Learnable Physics Engines for Inference and Control
Alvaro Sanchez · Nicolas Heess · Jost Springenberg · Josh Merel · Martin Riedmiller · Raia Hadsell · Peter Battaglia
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #85
An Alternative View: When Does SGD Escape Local Minima?
Bobby Kleinberg · Yuanzhi Li · Yang Yuan
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #86
Asynchronous Decentralized Parallel Stochastic Gradient Descent
Xiangru Lian · Wei Zhang · Ce Zhang · Ji Liu
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #87
An Estimation and Analysis Framework for the Rasch Model
Andrew Lan · Mung Chiang · Christoph Studer
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #88
Mitigating Bias in Adaptive Data Gathering via Differential Privacy
Seth V Neel · Aaron Roth
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #89
Local Private Hypothesis Testing: Chi-Square Tests
Marco Gaboardi · Ryan Rogers
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #90
Disentangling by Factorising
Hyunjik Kim · Andriy Mnih
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #91
Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning
Ronan Fruit · Matteo Pirotta · Alessandro Lazaric · Ronald Ortner
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #92
Learning to search with MCTSnets
Arthur Guez · Theophane Weber · Ioannis Antonoglou · Karen Simonyan · Oriol Vinyals · Daan Wierstra · Remi Munos · David Silver
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #93
Decoupled Parallel Backpropagation with Convergence Guarantee
Zhouyuan Huo · Bin Gu · Qian Yang · Heng Huang
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #94
On Learning Sparsely Used Dictionaries from Incomplete Samples
Thanh Nguyen · Akshay Soni · Chinmay Hegde
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #95
Variational Network Inference: Strong and Stable with Concrete Support
Amir Dezfouli · Edwin Bonilla · Richard Nock
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #96
Weakly Submodular Maximization Beyond Cardinality Constraints: Does Randomization Help Greedy?
Lin Chen · Moran Feldman · Amin Karbasi
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #97
Data Summarization at Scale: A Two-Stage Submodular Approach
Marko Mitrovic · Ehsan Kazemi · Morteza Zadimoghaddam · Amin Karbasi
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #98
Best Arm Identification in Linear Bandits with Linear Dimension Dependency
Chao Tao · Saúl A. Blanco · Yuan Zhou
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #99
Learning with Abandonment
Sven Schmit · Ramesh Johari
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #100
Hyperbolic Entailment Cones for Learning Hierarchical Embeddings
Octavian-Eugen Ganea · Gary Becigneul · Thomas Hofmann
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #101
Generative Temporal Models with Spatial Memory for Partially Observed Environments
Marco Fraccaro · Danilo J. Rezende · Yori Zwols · Alexander Pritzel · S. M. Ali Eslami · Fabio Viola
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #102
DiCE: The Infinitely Differentiable Monte Carlo Estimator
Jakob Foerster · Gregory Farquhar · Maruan Al-Shedivat · Tim Rocktäschel · Eric Xing · Shimon Whiteson
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #103
Orthogonal Recurrent Neural Networks with Scaled Cayley Transform
Kyle Helfrich · Devin Willmott · Qiang Ye
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #104
Least-Squares Temporal Difference Learning for the Linear Quadratic Regulator
Stephen Tu · Benjamin Recht
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #105
Spotlight: Optimizing Device Placement for Training Deep Neural Networks
Yuanxiang Gao · Department of Electrical and Computer Li Chen · Baochun Li
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #106
Universal Planning Networks: Learning Generalizable Representations for Visuomotor Control
Aravind Srinivas · Allan Jabri · Pieter Abbeel · Sergey Levine · Chelsea Finn
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #107
Coordinated Exploration in Concurrent Reinforcement Learning
Maria Dimakopoulou · Benjamin Van Roy
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #108
A probabilistic framework for multi-view feature learning with many-to-many associations via neural networks
Akifumi Okuno · Tetsuya Hada · Hidetoshi Shimodaira
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #109
Learning Steady-States of Iterative Algorithms over Graphs
Hanjun Dai · Zornitsa Kozareva · Bo Dai · Alex Smola · Le Song
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #110
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
Anish Athalye · Nicholas Carlini · David Wagner
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #111
Fair and Diverse DPP-Based Data Summarization
Elisa Celis · Vijay Keswani · Damian Straszak · Amit Jayant Deshpande · Tarun Kathuria · Nisheeth Vishnoi
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #112
Learning Implicit Generative Models with the Method of Learned Moments
Suman Ravuri · Shakir Mohamed · Mihaela Rosca · Oriol Vinyals
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #113
Chi-square Generative Adversarial Network
Chenyang Tao · Liqun Chen · Ricardo Henao · Jianfeng Feng · Lawrence Carin
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #114
Streaming Principal Component Analysis in Noisy Setting
Teodor Vanislavov Marinov · Poorya Mianjy · Raman Arora
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #115
Partial Optimality and Fast Lower Bounds for Weighted Correlation Clustering
Jan-Hendrik Lange · Andreas Karrenbauer · Bjoern Andres
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #116
SGD and Hogwild! Convergence Without the Bounded Gradients Assumption
Lam Nguyen · PHUONG HA NGUYEN · Marten van Dijk · Peter Richtarik · Katya Scheinberg · Martin Takac
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #117
Computational Optimal Transport: Complexity by Accelerated Gradient Descent Is Better Than by Sinkhorn's Algorithm
Pavel Dvurechenskii · Alexander Gasnikov · Alexey Kroshnin
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #118
Stability and Generalization of Learning Algorithms that Converge to Global Optima
Zachary Charles · Dimitris Papailiopoulos
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #119
Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces
Junhong Lin · Volkan Cevher
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #120
Adafactor: Adaptive Learning Rates with Sublinear Memory Cost
Noam Shazeer · Mitchell Stern
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #121
Fast Parametric Learning with Activation Memorization
Jack Rae · Chris Dyer · Peter Dayan · Timothy Lillicrap
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #122
Essentially No Barriers in Neural Network Energy Landscape
Felix Draxler · Kambis Veschgini · Manfred Salmhofer · Fred Hamprecht
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #123
Deep Linear Networks with Arbitrary Loss: All Local Minima Are Global
Thomas Laurent · James von Brecht
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #124
Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression
Haitao Liu · Jianfei Cai · Yi Wang · Yew Soon ONG
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #125
Bayesian Quadrature for Multiple Related Integrals
Xiaoyue Xi · Francois-Xavier Briol · Mark Girolami
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #126
Deep Predictive Coding Network for Object Recognition
Haiguang Wen · Kuan Han · Junxing Shi · Yizhen Zhang · Eugenio Culurciello · Zhongming Liu
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #127
Neural Inverse Rendering for General Reflectance Photometric Stereo
Tatsunori Taniai · Takanori Maehara
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #128
On the Relationship between Data Efficiency and Error for Uncertainty Sampling
Stephen Mussmann · Percy Liang
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #129
Selecting Representative Examples for Program Synthesis
Yewen Pu · Zachery Miranda · Armando Solar-Lezama · Leslie Kaelbling
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #130
Conditional Neural Processes
Marta Garnelo · Dan Rosenbaum · Chris Maddison · Tiago Ramalho · David Saxton · Murray Shanahan · Yee Teh · Danilo J. Rezende · S. M. Ali Eslami
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #131
Hierarchical Long-term Video Prediction without Supervision
Nevan Wichers · Ruben Villegas · Dumitru Erhan · Honglak Lee
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #132
Adversarial Risk and the Dangers of Evaluating Against Weak Attacks
Jonathan Uesato · Brendan O'Donoghue · Pushmeet Kohli · Aäron van den Oord
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #133
A Classification-Based Study of Covariate Shift in GAN Distributions
Shibani Santurkar · Ludwig Schmidt · Aleksander Madry
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #134
Gated Path Planning Networks
Lisa Lee · Emilio Parisotto · Devendra Singh Chaplot · Eric Xing · Ruslan Salakhutdinov
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #135
Automatic Goal Generation for Reinforcement Learning Agents
Carlos Florensa · David Held · Xinyang Geng · Pieter Abbeel
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #136
ADMM and Accelerated ADMM as Continuous Dynamical Systems
Guilherme Franca · Daniel Robinson · Rene Vidal
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #137
Dissipativity Theory for Accelerating Stochastic Variance Reduction: A Unified Analysis of SVRG and Katyusha Using Semidefinite Programs
Bin Hu · Stephen Wright · Laurent Lessard
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #138
Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing
Davide Bacciu · Federico Errica · Alessio Micheli
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #139
Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry
Maximillian Nickel · Douwe Kiela
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #140
Fast Variance Reduction Method with Stochastic Batch Size
University of California Xuanqing Liu · Cho-Jui Hsieh
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #141
Lyapunov Functions for First-Order Methods: Tight Automated Convergence Guarantees
Adrien Taylor · Bryan Van Scoy · Laurent Lessard
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #142
Nonparametric Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information
Yichong Xu · Hariank Muthakana · Sivaraman Balakrishnan · Aarti Singh · Artur Dubrawski
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #143
The Well-Tempered Lasso
Yuanzhi Li · Yoram Singer
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #144
Transfer Learning via Learning to Transfer
Ying WEI · Yu Zhang · Junzhou Huang · Qiang Yang
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #145
Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing—and Back
Elliot Meyerson · Risto Miikkulainen
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #146
Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model
Hideaki Imamura · Issei Sato · Masashi Sugiyama
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #147
Deep One-Class Classification
Lukas Ruff · Nico Görnitz · Lucas Deecke · Shoaib Ahmed Siddiqui · Robert Vandermeulen · Alexander Binder · Emmanuel Müller · Marius Kloft
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #148
Binary Partitions with Approximate Minimum Impurity
Eduardo Laber · Marco Molinaro · Felipe de A. Mello Pereira
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #149
Beyond 1/2-Approximation for Submodular Maximization on Massive Data Streams
Ashkan Norouzi-Fard · Jakub Tarnawski · Slobodan Mitrovic · Amir Zandieh · Aidasadat Mousavifar · Ola Svensson
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #150
Yes, but Did It Work?: Evaluating Variational Inference
Yuling Yao · Aki Vehtari · Daniel Simpson · Andrew Gelman
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #151
Black-Box Variational Inference for Stochastic Differential Equations
Tom Ryder · Andrew Golightly · Stephen McGough · Dennis Prangle
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #152
Online Convolutional Sparse Coding with Sample-Dependent Dictionary
Yaqing WANG · Quanming Yao · James Kwok · Lionel NI
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #153
Learning to Speed Up Structured Output Prediction
Xingyuan Pan · Vivek Srikumar
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #154
Differentially Private Identity and Equivalence Testing of Discrete Distributions
Maryam Aliakbarpour · Ilias Diakonikolas · MIT Ronitt Rubinfeld
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #155
Information Theoretic Guarantees for Empirical Risk Minimization with Applications to Model Selection and Large-Scale Optimization
Ibrahim Alabdulmohsin
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #156
BOCK : Bayesian Optimization with Cylindrical Kernels
ChangYong Oh · Efstratios Gavves · Max Welling
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #157
BOHB: Robust and Efficient Hyperparameter Optimization at Scale
Stefan Falkner · Aaron Klein · Frank Hutter
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #158
Distributed Nonparametric Regression under Communication Constraints
Yuancheng Zhu · John Lafferty
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #159
Optimal Tuning for Divide-and-conquer Kernel Ridge Regression with Massive Data
Ganggang Xu · Zuofeng Shang · Guang Cheng
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #160
WHInter: A Working set algorithm for High-dimensional sparse second order Interaction models
Marine LE MORVAN · Jean-Philippe Vert
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #161
Safe Element Screening for Submodular Function Minimization
Weizhong Zhang · Bin Hong · Lin Ma · Wei Liu · Tong Zhang
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #162
Feedback-Based Tree Search for Reinforcement Learning
Daniel Jiang · Emmanuel Ekwedike · Han Liu
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #163
Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement
Andre Barreto · Diana Borsa · John Quan · Tom Schaul · David Silver · Matteo Hessel · Daniel J. Mankowitz · Augustin Zidek · Remi Munos
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #164
Data-Dependent Stability of Stochastic Gradient Descent
Ilja Kuzborskij · Christoph Lampert
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #165
LeapsAndBounds: A Method for Approximately Optimal Algorithm Configuration
Gellért Weisz · Andras Gyorgy · Csaba Szepesvari
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #166
Scalable Deletion-Robust Submodular Maximization: Data Summarization with Privacy and Fairness Constraints
Ehsan Kazemi · Morteza Zadimoghaddam · Amin Karbasi
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #167
Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization
Jinghui Chen · Pan Xu · Lingxiao Wang · Jian Ma · Quanquan Gu
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #168
Comparing Dynamics: Deep Neural Networks versus Glassy Systems
Marco Baity-Jesi · Levent Sagun · Mario Geiger · Stefano Spigler · Gerard Arous · Chiara Cammarota · Yann LeCun · Matthieu Wyart · Giulio Biroli
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #169
An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks
Qianxiao Li · IHPC Shuji Hao
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #170
Not All Samples Are Created Equal: Deep Learning with Importance Sampling
Angelos Katharopoulos · Francois Fleuret
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #171
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks
Lechao Xiao · Yasaman Bahri · Jascha Sohl-Dickstein · Samuel Schoenholz · Jeffrey Pennington
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #172
Path Consistency Learning in Tsallis Entropy Regularized MDPs
Yinlam Chow · Ofir Nachum · Mohammad Ghavamzadeh
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #173
Lipschitz Continuity in Model-based Reinforcement Learning
Kavosh Asadi · Dipendra Misra · Michael L. Littman
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #174
Bounds on the Approximation Power of Feedforward Neural Networks
Mohammad Mehrabi · Aslan Tchamkerten · MANSOOR I YOUSEFI
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #174
Linear Spectral Estimators and an Application to Phase Retrieval
Ramina Ghods · Andrew Lan · Tom Goldstein · Christoph Studer
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #175
Testing Sparsity over Known and Unknown Bases
Siddharth Barman · Arnab Bhattacharyya · Suprovat Ghoshal
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #176
Inference Suboptimality in Variational Autoencoders
Chris Cremer · Xuechen Li · David Duvenaud
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #177
Semi-Implicit Variational Inference
Mingzhang Yin · Mingyuan Zhou
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #178
Variance Regularized Counterfactual Risk Minimization via Variational Divergence Minimization
Hang Wu · May Wang
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #179
Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design
Ahmed M. Alaa Ibrahim · M van der Schaar
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #180
A Semantic Loss Function for Deep Learning with Symbolic Knowledge
Jingyi Xu · Zilu Zhang · Tal Friedman · Yitao Liang · Guy Van den Broeck
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #181
Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization
Jiong Zhang · Qi Lei · Inderjit Dhillon
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #182
An Efficient Semismooth Newton based Algorithm for Convex Clustering
Yancheng Yuan · Defeng Sun · Kim-Chuan Toh
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #183
Lightweight Stochastic Optimization for Minimizing Finite Sums with Infinite Data
Shuai Zheng · James Kwok
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #184
Exploiting the Potential of Standard Convolutional Autoencoders for Image Restoration by Evolutionary Search
Masanori SUGANUMA · Mete Ozay · Takayuki Okatani
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #185
Efficient Neural Architecture Search via Parameters Sharing
Hieu Pham · Melody Guan · Barret Zoph · Quoc Le · Jeff Dean
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #186
Non-convex Conditional Gradient Sliding
chao qu · Yan Li · Huan Xu
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #187
Stochastic Variance-Reduced Cubic Regularized Newton Method
Dongruo Zhou · Pan Xu · Quanquan Gu
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #188
On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization
Sanjeev Arora · Nadav Cohen · Elad Hazan
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #189
The Dynamics of Learning: A Random Matrix Approach
Zhenyu Liao · Romain Couillet
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #190
Learning K-way D-dimensional Discrete Codes for Compact Embedding Representations
Ting Chen · Martin Min · Yizhou Sun
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #191
Discovering Interpretable Representations for Both Deep Generative and Discriminative Models
Tameem Adel · Zoubin Ghahramani · Adrian Weller
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #192
Continuous-Time Flows for Efficient Inference and Density Estimation
Changyou Chen · Chunyuan Li · Liquan Chen · Wenlin Wang · Yunchen Pu · Lawrence Carin
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #193
Tighter Variational Bounds are Not Necessarily Better
Tom Rainforth · Adam Kosiorek · Tuan Anh Le · Chris Maddison · Maximilian Igl · Frank Wood · Yee Whye Teh
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #194
PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning
Yunbo Wang · Zhifeng Gao · Mingsheng Long · Jianmin Wang · Philip Yu
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #195
RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks
Jinsung Yoon · James Jordon · Mihaela van der Schaar
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #196
Differentiable Compositional Kernel Learning for Gaussian Processes
Shengyang Sun · Guodong Zhang · Chaoqi Wang · Wenyuan Zeng · Jiaman Li · Roger Grosse
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #197
Markov Modulated Gaussian Cox Processes for Semi-Stationary Intensity Modeling of Events Data
Minyoung Kim
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #198
Improved Regret Bounds for Thompson Sampling in Linear Quadratic Control Problems
Marc Abeille · Alessandro Lazaric
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #199
Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches
Simon Olofsson · Marc P Deisenroth · Ruth Misener
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #200
Anonymous Walk Embeddings
Sergey Ivanov · Evgeny Burnaev
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #201
Improving Optimization in Models With Continuous Symmetry Breaking
Robert Bamler · Stephan Mandt
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #202
Conditional Noise-Contrastive Estimation of Unnormalised Models
Ciwan Ceylan · Michael Gutmann
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #203
Canonical Tensor Decomposition for Knowledge Base Completion
Timothee Lacroix · Nicolas Usunier · Guillaume R Obozinski
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #204
The Power of Interpolation: Understanding the Effectiveness of SGD in Modern Over-parametrized Learning
Siyuan Ma · Raef Bassily · Mikhail Belkin
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #205
A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates
Kaiwen Zhou · Fanhua Shang · James Cheng
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #206
Escaping Saddles with Stochastic Gradients
Hadi Daneshmand · Jonas Kohler · Aurelien Lucchi · Thomas Hofmann
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #207
$D^2$: Decentralized Training over Decentralized Data
Hanlin Tang · Xiangru Lian · Ming Yan · Ce Zhang · Ji Liu
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #208
Machine Theory of Mind
Neil Rabinowitz · Frank Perbet · Francis Song · Chiyuan Zhang · S. M. Ali Eslami · Matthew Botvinick
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #209
Been There, Done That: Meta-Learning with Episodic Recall
Samuel Ritter · Jane Wang · Zeb Kurth-Nelson · Siddhant Jayakumar · Charles Blundell · Razvan Pascanu · Matthew Botvinick
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #210
Faster Derivative-Free Stochastic Algorithm for Shared Memory Machines
Bin Gu · Zhouyuan Huo · Cheng Deng · Heng Huang
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #211
Coded Sparse Matrix Multiplication
Sinong Wang · Jiashang Liu · Ness Shroff
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #212
Augment and Reduce: Stochastic Inference for Large Categorical Distributions
Francisco Ruiz · Michalis Titsias · Adji Bousso Dieng · David Blei
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #213
Efficient Gradient-Free Variational Inference using Policy Search
Oleg Arenz · Gerhard Neumann · Mingjun Zhong
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #214
Fixing a Broken ELBO
Alexander Alemi · Ben Poole · Ian Fischer · Joshua V Dillon · Rif Saurous · Kevin Murphy
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #215
Variational Inference and Model Selection with Generalized Evidence Bounds
Liqun Chen · Chenyang Tao · RUIYI ZHANG · Ricardo Henao · Lawrence Carin
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #216
The Generalization Error of Dictionary Learning with Moreau Envelopes
ALEXANDROS GEORGOGIANNIS
Poster
Wed Jul 11th 06:15 -- 09:00 PM @ Hall B #217
Network Global Testing by Counting Graphlets
Jiashun Jin · Zheng Ke · Shengming Luo