Proceedings of Machine Learning Research

Proceedings of Machine Learning Research
Improved Regret Bounds for Thompson Sampling in Linear Quadratic Control Problems
Marc Abeille, Alessandro Lazaric ; PMLR 80:1-9
State Abstractions for Lifelong Reinforcement Learning
David Abel, Dilip Arumugam, Lucas Lehnert, Michael Littman ; PMLR 80:10-19
Policy and Value Transfer in Lifelong Reinforcement Learning
David Abel, Yuu Jinnai, Sophie Yue Guo, George Konidaris, Michael Littman ; PMLR 80:20-29
INSPECTRE: Privately Estimating the Unseen
Jayadev Acharya, Gautam Kamath, Ziteng Sun, Huanyu Zhang ; PMLR 80:30-39
Learning Representations and Generative Models for 3D Point Clouds
Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, Leonidas Guibas ; PMLR 80:40-49
Discovering Interpretable Representations for Both Deep Generative and Discriminative Models
Tameem Adel, Zoubin Ghahramani, Adrian Weller ; PMLR 80:50-59
A Reductions Approach to Fair Classification
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudik, John Langford, Hanna Wallach ; PMLR 80:60-69
MISSION: Ultra Large-Scale Feature Selection using Count-Sketches
Amirali Aghazadeh, Ryan Spring, Daniel Lejeune, Gautam Dasarathy, Anshumali Shrivastava, baraniuk ; PMLR 80:80-88
Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models
Raj Agrawal, Caroline Uhler, Tamara Broderick ; PMLR 80:89-98
Proportional Allocation: Simple, Distributed, and Diverse Matching with High Entropy
Shipra Agrawal, Morteza Zadimoghaddam, Vahab Mirrokni ; PMLR 80:99-108
Bucket Renormalization for Approximate Inference
Sungsoo Ahn, Michael Chertkov, Adrian Weller, Jinwoo Shin ; PMLR 80:109-118
oi-VAE: Output Interpretable VAEs for Nonlinear Group Factor Analysis
Samuel K. Ainsworth, Nicholas J. Foti, Adrian K. C. Lee, Emily B. Fox ; PMLR 80:119-128
Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design
Ahmed Alaa, Mihaela Schaar ; PMLR 80:129-138
AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning
Ahmed Alaa, Mihaela Schaar ; PMLR 80:139-148
Information Theoretic Guarantees for Empirical Risk Minimization with Applications to Model Selection and Large-Scale Optimization
Ibrahim Alabdulmohsin ; PMLR 80:149-158
Subspace Embedding and Linear Regression with Orlicz Norm
Alexandr Andoni, Chengyu Lin, Ying Sheng, Peilin Zhong, Ruiqi Zhong ; PMLR 80:224-233
Efficient Gradient-Free Variational Inference using Policy Search
Oleg Arenz, Mingjun Zhong, Gerhard Neumann ; PMLR 80:234-243
On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization
Sanjeev Arora, Nadav Cohen, Elad Hazan ; PMLR 80:244-253
Stronger Generalization Bounds for Deep Nets via a Compression Approach
Sanjeev Arora, Rong Ge, Behnam Neyshabur, Yi Zhang ; PMLR 80:254-263
Lipschitz Continuity in Model-based Reinforcement Learning
Kavosh Asadi, Dipendra Misra, Michael Littman ; PMLR 80:264-273
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
Anish Athalye, Nicholas Carlini, David Wagner ; PMLR 80:274-283
Clustering Semi-Random Mixtures of Gaussians
Pranjal Awasthi, Aravindan Vijayaraghavan ; PMLR 80:294-303
Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing
Davide Bacciu, Federico Errica, Alessio Micheli ; PMLR 80:304-313
Greed is Still Good: Maximizing Monotone Submodular+Supermodular (BP) Functions
Wenruo Bai, Jeffrey Bilmes ; PMLR 80:314-323
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Comparing Dynamics: Deep Neural Networks versus Glassy Systems
Marco Baity-Jesi, Levent Sagun, Mario Geiger, Stefano Spigler, Gerard Ben Arous, Chiara Cammarota, Yann LeCun, Matthieu Wyart, Giulio Biroli ; PMLR 80:324-333
SMAC: Simultaneous Mapping and Clustering Using Spectral Decompositions
Chandrajit Bajaj, Tingran Gao, Zihang He, Qixing Huang, Zhenxiao Liang ; PMLR 80:334-343
A Boo(n) for Evaluating Architecture Performance
Ondrej Bajgar, Rudolf Kadlec, Jan Kleindienst ; PMLR 80:344-352
The Mechanics of n-Player Differentiable Games
David Balduzzi, Sebastien Racaniere, James Martens, Jakob Foerster, Karl Tuyls, Thore Graepel ; PMLR 80:363-372
Spline Filters For End-to-End Deep Learning
Randall Balestriero, Romain Cosentino, Herve Glotin, Richard Baraniuk ; PMLR 80:373-382
A Spline Theory of Deep Networks
Randall Balestriero, baraniuk ; PMLR 80:383-392
Approximation Guarantees for Adaptive Sampling
Eric Balkanski, Yaron Singer ; PMLR 80:393-402
Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising
Borja Balle, Yu-Xiang Wang ; PMLR 80:403-412
Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients
Lukas Balles, Philipp Hennig ; PMLR 80:413-422
Differentially Private Database Release via Kernel Mean Embeddings
Matej Balog, Ilya Tolstikhin, Bernhard Schölkopf ; PMLR 80:423-431
Improving Optimization in Models With Continuous Symmetry Breaking
Robert Bamler, Stephan Mandt ; PMLR 80:432-441
Improved Training of Generative Adversarial Networks using Representative Features
Duhyeon Bang, Hyunjung Shim ; PMLR 80:442-451
Using Inherent Structures to design Lean 2-layer RBMs
Abhishek Bansal, Abhinav Anand, Chiranjib Bhattacharyya ; PMLR 80:452-460
Classification from Pairwise Similarity and Unlabeled Data
Han Bao, Gang Niu, Masashi Sugiyama ; PMLR 80:461-470
Bayesian Optimization of Combinatorial Structures
Ricardo Baptista, Matthias Poloczek ; PMLR 80:471-480
Learning to Coordinate with Coordination Graphs in Repeated Single-Stage Multi-Agent Decision Problems
Eugenio Bargiacchi, Timothy Verstraeten, Diederik Roijers, Ann Nowé, Hado Hasselt ; PMLR 80:491-499
Testing Sparsity over Known and Unknown Bases
Siddharth Barman, Arnab Bhattacharyya, Suprovat Ghoshal ; PMLR 80:500-509
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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 Mankowitz, Augustin Zidek, Remi Munos ; PMLR 80:510-519
Gradient descent with identity initialization efficiently learns positive definite linear transformations
Peter Bartlett, Dave Helmbold, Phil Long ; PMLR 80:520-529
Mutual Information Neural Estimation
Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeshwar, Sherjil Ozair, Yoshua Bengio, Devon Hjelm, Aaron Courville ; PMLR 80:530-539
To Understand Deep Learning We Need to Understand Kernel Learning
Mikhail Belkin, Siyuan Ma, Soumik Mandal ; PMLR 80:540-548
Understanding and Simplifying One-Shot Architecture Search
Gabriel Bender, Pieter-Jan Kindermans, Barret Zoph, Vijay Vasudevan, Quoc Le ; PMLR 80:549-558
SIGNSGD: Compressed Optimisation for Non-Convex Problems
Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, Animashree Anandkumar ; PMLR 80:559-568
Distributed Clustering via LSH Based Data Partitioning
Aditya Bhaskara, Maheshakya Wijewardena ; PMLR 80:569-578
Autoregressive Convolutional Neural Networks for Asynchronous Time Series
Mikolaj Binkowski, Gautier Marti, Philippe Donnat ; PMLR 80:579-588
Adaptive Sampled Softmax with Kernel Based Sampling
Guy Blanc, Steffen Rendle ; PMLR 80:589-598
Optimizing the Latent Space of Generative Networks
Piotr Bojanowski, Armand Joulin, David Lopez-Pas, Arthur Szlam ; PMLR 80:599-608
NetGAN: Generating Graphs via Random Walks
Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, Stephan Günnemann ; PMLR 80:609-618
A Progressive Batching L-BFGS Method for Machine Learning
Raghu Bollapragada, Dheevatsa Mudigere, Jorge Nocedal, Hao-Jun Michael Shi, Ping Tak Peter Tang ; PMLR 80:619-628
Path-Level Network Transformation for Efficient Architecture Search
Han Cai, Jiacheng Yang, Weinan Zhang, Song Han, Yong Yu ; PMLR 80:677-686
Improved Large-Scale Graph Learning through Ridge Spectral Sparsification
Daniele Calandriello, Ioannis Koutis, Alessandro Lazaric, Michal Valko ; PMLR 80:687-696
Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent
Trevor Campbell, Tamara Broderick ; PMLR 80:697-705
Adversarial Learning with Local Coordinate Coding
Jiezhang Cao, Yong Guo, Qingyao Wu, Chunhua Shen, Junzhou Huang, Mingkui Tan ; PMLR 80:706-714
Fair and Diverse DPP-Based Data Summarization
Elisa Celis, Vijay Keswani, Damian Straszak, Amit Deshpande, Tarun Kathuria, Nisheeth Vishnoi ; PMLR 80:715-724
Conditional Noise-Contrastive Estimation of Unnormalised Models
Ciwan Ceylan, Michael U. Gutmann ; PMLR 80:725-733
Adversarial Time-to-Event Modeling
Paidamoyo Chapfuwa, Chenyang Tao, Chunyuan Li, Courtney Page, Benjamin Goldstein, Lawrence Carin Duke, Ricardo Henao ; PMLR 80:734-743
Stability and Generalization of Learning Algorithms that Converge to Global Optima
Zachary Charles, Dimitris Papailiopoulos ; PMLR 80:744-753
On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo
Niladri Chatterji, Nicolas Flammarion, Yian Ma, Peter Bartlett, Michael Jordan ; PMLR 80:763-772
Hierarchical Clustering with Structural Constraints
Vaggos Chatziafratis, Rad Niazadeh, Moses Charikar ; PMLR 80:773-782
Hierarchical Deep Generative Models for Multi-Rate Multivariate Time Series
Zhengping Che, Sanjay Purushotham, Guangyu Li, Bo Jiang, Yan Liu ; PMLR 80:783-792
GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks
Zhao Chen, Vijay Badrinarayanan, Chen-Yu Lee, Andrew Rabinovich ; PMLR 80:793-802
Weakly Submodular Maximization Beyond Cardinality Constraints: Does Randomization Help Greedy?
Lin Chen, Moran Feldman, Amin Karbasi ; PMLR 80:803-812
Projection-Free Online Optimization with Stochastic Gradient: From Convexity to Submodularity
Lin Chen, Christopher Harshaw, Hamed Hassani, Amin Karbasi ; PMLR 80:813-822
Continuous-Time Flows for Efficient Inference and Density Estimation
Changyou Chen, Chunyuan Li, Liquan Chen, Wenlin Wang, Yunchen Pu, Lawrence Carin Duke ; PMLR 80:823-832
Scalable Bilinear Learning Using State and Action Features
Yichen Chen, Lihong Li, Mengdi Wang ; PMLR 80:833-842
Learning K-way D-dimensional Discrete Codes for Compact Embedding Representations
Ting Chen, Martin Renqiang Min, Yizhou Sun ; PMLR 80:853-862
PixelSNAIL: An Improved Autoregressive Generative Model
Xi Chen, Nikhil Mishra, Mostafa Rohaninejad, Pieter Abbeel ; PMLR 80:863-871
Dynamical Isometry and a Mean Field Theory of RNNs: Gating Enables Signal Propagation in Recurrent Neural Networks
Minmin Chen, Jeffrey Pennington, Samuel Schoenholz ; PMLR 80:872-881
Learning to Explain: An Information-Theoretic Perspective on Model Interpretation
Jianbo Chen, Le Song, Martin Wainwright, Michael Jordan ; PMLR 80:882-891
Variational Inference and Model Selection with Generalized Evidence Bounds
Liqun Chen, Chenyang Tao, Ruiyi Zhang, Ricardo Henao, Lawrence Carin Duke ; PMLR 80:892-901
DRACO: Byzantine-resilient Distributed Training via Redundant Gradients
Lingjiao Chen, Hongyi Wang, Zachary Charles, Dimitris Papailiopoulos ; PMLR 80:902-911
SADAGRAD: Strongly Adaptive Stochastic Gradient Methods
Zaiyi Chen, Yi Xu, Enhong Chen, Tianbao Yang ; PMLR 80:912-920
Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization
Jinghui Chen, Pan Xu, Lingxiao Wang, Jian Ma, Quanquan Gu ; PMLR 80:921-930
End-to-End Learning for the Deep Multivariate Probit Model
Di Chen, Yexiang Xue, Carla Gomes ; PMLR 80:931-940
Stochastic Training of Graph Convolutional Networks with Variance Reduction
Jianfei Chen, Jun Zhu, Le Song ; PMLR 80:941-949
Extreme Learning to Rank via Low Rank Assumption
Minhao Cheng, Ian Davidson, Cho-Jui Hsieh ; PMLR 80:950-959
Learning a Mixture of Two Multinomial Logits
Flavio Chierichetti, Ravi Kumar, Andrew Tomkins ; PMLR 80:960-968
Structured Evolution with Compact Architectures for Scalable Policy Optimization
Krzysztof Choromanski, Mark Rowland, Vikas Sindhwani, Richard E. Turner, Adrian Weller ; PMLR 80:969-977
Path Consistency Learning in Tsallis Entropy Regularized MDPs
Yinlam Chow, Ofir Nachum, Mohammad Ghavamzadeh ; PMLR 80:978-987
An Iterative, Sketching-based Framework for Ridge Regression
Agniva Chowdhury, Jiasen Yang, Petros Drineas ; PMLR 80:988-997
Inference Suboptimality in Variational Autoencoders
Chris Cremer, Xuechen Li, David Duvenaud ; PMLR 80:1086-1094
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Mix & Match Agent Curricula for Reinforcement Learning
Wojciech Marian Czarnecki, Siddhant M. Jayakumar, Max Jaderberg, Leonard Hasenclever, Yee Whye Teh, Nicolas Heess, Simon Osindero, Razvan Pascanu ; PMLR 80:1095-1103
Implicit Quantile Networks for Distributional Reinforcement Learning
Will Dabney, Georg Ostrovski, David Silver, Remi Munos ; PMLR 80:1104-1113
Learning Steady-States of Iterative Algorithms over Graphs
Hanjun Dai, Zornitsa Kozareva, Bo Dai, Alex Smola, Le Song ; PMLR 80:1114-1122
Adversarial Attack on Graph Structured Data
Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, Le Song ; PMLR 80:1123-1132
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SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation
Bo Dai, Albert Shaw, Lihong Li, Lin Xiao, Niao He, Zhen Liu, Jianshu Chen, Le Song ; PMLR 80:1133-1142
Compressing Neural Networks using the Variational Information Bottleneck
Bin Dai, Chen Zhu, Baining Guo, David Wipf ; PMLR 80:1143-1152
Asynchronous Byzantine Machine Learning (the case of SGD)
Georgios Damaskinos, El Mahdi El Mhamdi, Rachid Guerraoui, Rhicheek Patra, Mahsa Taziki ; PMLR 80:1153-1162
Escaping Saddles with Stochastic Gradients
Hadi Daneshmand, Jonas Kohler, Aurelien Lucchi, Thomas Hofmann ; PMLR 80:1163-1172
Minibatch Gibbs Sampling on Large Graphical Models
Christopher De Sa, Vincent Chen, Wing Wong ; PMLR 80:1173-1181
Stochastic Video Generation with a Learned Prior
Emily Denton, Rob Fergus ; PMLR 80:1182-1191
Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning
Stefan Depeweg, Jose-Miguel Hernandez-Lobato, Finale Doshi-Velez, Steffen Udluft ; PMLR 80:1192-1201
Accurate Inference for Adaptive Linear Models
Yash Deshpande, Lester Mackey, Vasilis Syrgkanis, Matt Taddy ; PMLR 80:1202-1211
Variational Network Inference: Strong and Stable with Concrete Support
Amir Dezfouli, Edwin V. Bonilla, Richard Nock ; PMLR 80:1212-1221
Modeling Sparse Deviations for Compressed Sensing using Generative Models
Manik Dhar, Aditya Grover, Stefano Ermon ; PMLR 80:1222-1231
Alternating Randomized Block Coordinate Descent
Jelena Diakonikolas, Lorenzo Orecchia ; PMLR 80:1232-1240
Learning to Act in Decentralized Partially Observable MDPs
Jilles Dibangoye, Olivier Buffet ; PMLR 80:1241-1250
Noisin: Unbiased Regularization for Recurrent Neural Networks
Adji Bousso Dieng, Rajesh Ranganath, Jaan Altosaar, David Blei ; PMLR 80:1251-1260
Discovering and Removing Exogenous State Variables and Rewards for Reinforcement Learning
Thomas Dietterich, George Trimponias, Zhitang Chen ; PMLR 80:1261-1269
Coordinated Exploration in Concurrent Reinforcement Learning
Maria Dimakopoulou, Benjamin Van Roy ; PMLR 80:1270-1278
Randomized Block Cubic Newton Method
Nikita Doikov, Peter Richtarik, University Edinburgh ; PMLR 80:1289-1297
Low-Rank Riemannian Optimization on Positive Semidefinite Stochastic Matrices with Applications to Graph Clustering
Ahmed Douik, Babak Hassibi ; PMLR 80:1298-1307
Essentially No Barriers in Neural Network Energy Landscape
Felix Draxler, Kambis Veschgini, Manfred Salmhofer, Fred Hamprecht ; PMLR 80:1308-1317
Weakly Consistent Optimal Pricing Algorithms in Repeated Posted-Price Auctions with Strategic Buyer
Alexey Drutsa ; PMLR 80:1318-1327
On the Power of Over-parametrization in Neural Networks with Quadratic Activation
Simon S. Du, Jason D. Lee ; PMLR 80:1328-1337
Gradient Descent Learns One-hidden-layer CNN: Don’t be Afraid of Spurious Local Minima
Simon S. Du, Jason D. Lee, Yuandong Tian, Aarti Singh, Barnabas Poczos ; PMLR 80:1338-1347
Investigating Human Priors for Playing Video Games
Rachit Dubey, Pulkit Agrawal, Deepak Pathak, Tom Griffiths, Alexei Efros ; PMLR 80:1348-1356
A Distributed Second-Order Algorithm You Can Trust
Celestine Duenner, Aurelien Lucchi, Matilde Gargiani, An Bian, Thomas Hofmann, Martin Jaggi ; PMLR 80:1357-1365
Computational Optimal Transport: Complexity by Accelerated Gradient Descent Is Better Than by Sinkhorn’s Algorithm
Pavel Dvurechensky, Alexander Gasnikov, Alexey Kroshnin ; PMLR 80:1366-1375
Entropy-SGD optimizes the prior of a PAC-Bayes bound: Generalization properties of Entropy-SGD and data-dependent priors
Gintare Karolina Dziugaite, Daniel Roy ; PMLR 80:1376-1385
Beyond the One-Step Greedy Approach in Reinforcement Learning
Yonathan Efroni, Gal Dalal, Bruno Scherrer, Shie Mannor ; PMLR 80:1386-1395
Parallel and Streaming Algorithms for K-Core Decomposition
Hossein Esfandiari, Silvio Lattanzi, Vahab Mirrokni ; PMLR 80:1396-1405
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IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
Lasse Espeholt, Hubert Soyer, Remi Munos, Karen Simonyan, Volodymyr Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, Shane Legg, Koray Kavukcuoglu ; PMLR 80:1406-1415
Scalable Gaussian Processes with Grid-Structured Eigenfunctions (GP-GRIEF)
Trefor Evans, Prasanth Nair ; PMLR 80:1416-1425
The Limits of Maxing, Ranking, and Preference Learning
Moein Falahatgar, Ayush Jain, Alon Orlitsky, Venkatadheeraj Pichapati, Vaishakh Ravindrakumar ; PMLR 80:1426-1435
BOHB: Robust and Efficient Hyperparameter Optimization at Scale
Stefan Falkner, Aaron Klein, Frank Hutter ; PMLR 80:1436-1445
More Robust Doubly Robust Off-policy Evaluation
Mehrdad Farajtabar, Yinlam Chow, Mohammad Ghavamzadeh ; PMLR 80:1446-1455
Efficient and Consistent Adversarial Bipartite Matching
Rizal Fathony, Sima Behpour, Xinhua Zhang, Brian Ziebart ; PMLR 80:1456-1465
Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator
Maryam Fazel, Rong Ge, Sham Kakade, Mehran Mesbahi ; PMLR 80:1466-1475
CRVI: Convex Relaxation for Variational Inference
Ghazal Fazelnia, John Paisley ; PMLR 80:1476-1484
Nonparametric variable importance using an augmented neural network with multi-task learning
Jean Feng, Brian D. Williamson, Marco Carone, Noah Simon ; PMLR 80:1495-1504
Closed-form Marginal Likelihood in Gamma-Poisson Matrix Factorization
Louis Filstroff, Alberto Lumbreras, Cédric Févotte ; PMLR 80:1505-1513
Automatic Goal Generation for Reinforcement Learning Agents
Carlos Florensa, David Held, Xinyang Geng, Pieter Abbeel ; PMLR 80:1514-1523
DiCE: The Infinitely Differentiable Monte Carlo Estimator
Jakob Foerster, Gregory Farquhar, Maruan Al-Shedivat, Tim Rocktäschel, Eric Xing, Shimon Whiteson ; PMLR 80:1524-1533
Practical Contextual Bandits with Regression Oracles
Dylan Foster, Alekh Agarwal, Miroslav Dudik, Haipeng Luo, Robert Schapire ; PMLR 80:1534-1543
Generative Temporal Models with Spatial Memory for Partially Observed Environments
Marco Fraccaro, Danilo Rezende, Yori Zwols, Alexander Pritzel, S. M. Ali Eslami, Fabio Viola ; PMLR 80:1544-1553
ADMM and Accelerated ADMM as Continuous Dynamical Systems
Guilherme Franca, Daniel Robinson, Rene Vidal ; PMLR 80:1554-1562
Bilevel Programming for Hyperparameter Optimization and Meta-Learning
Luca Franceschi, Paolo Frasconi, Saverio Salzo, Riccardo Grazzi, Massimiliano Pontil ; PMLR 80:1563-1572
Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning
Ronan Fruit, Matteo Pirotta, Alessandro Lazaric, Ronald Ortner ; PMLR 80:1573-1581
Addressing Function Approximation Error in Actor-Critic Methods
Scott Fujimoto, Herke Hoof, David Meger ; PMLR 80:1582-1591
Local Private Hypothesis Testing: Chi-Square Tests
Marco Gaboardi, Ryan Rogers ; PMLR 80:1612-1621
Inductive Two-layer Modeling with Parametric Bregman Transfer
Vignesh Ganapathiraman, Zhan Shi, Xinhua Zhang, Yaoliang Yu ; PMLR 80:1622-1631
Hyperbolic Entailment Cones for Learning Hierarchical Embeddings
Octavian-Eugen Ganea, Gary Becigneul, Thomas Hofmann ; PMLR 80:1632-1641
Parameterized Algorithms for the Matrix Completion Problem
Robert Ganian, Iyad Kanj, Sebastian Ordyniak, Stefan Szeider ; PMLR 80:1642-1651
Synthesizing Programs for Images using Reinforced Adversarial Learning
Yaroslav Ganin, Tejas Kulkarni, Igor Babuschkin, S. M. Ali Eslami, Oriol Vinyals ; PMLR 80:1652-1661
Spotlight: Optimizing Device Placement for Training Deep Neural Networks
Yuanxiang Gao, Li Chen, Baochun Li ; PMLR 80:1662-1670
Parallel Bayesian Network Structure Learning
Tian Gao, Dennis Wei ; PMLR 80:1671-1680
Structured Output Learning with Abstention: Application to Accurate Opinion Prediction
Alexandre Garcia, Chloé Clavel, Slim Essid, Florence d’Alche-Buc ; PMLR 80:1681-1689
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Conditional Neural Processes
Marta Garnelo, Dan Rosenbaum, Christopher Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh, Danilo Rezende, S. M. Ali Eslami ; PMLR 80:1690-1699
Temporal Poisson Square Root Graphical Models
Sinong Geng, Zhaobin Kuang, Peggy Peissig, David Page ; PMLR 80:1700-1709
The Generalization Error of Dictionary Learning with Moreau Envelopes
Alexandros Georgogiannis ; PMLR 80:1710-1718
Budgeted Experiment Design for Causal Structure Learning
AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Elias Bareinboim ; PMLR 80:1719-1728
Linear Spectral Estimators and an Application to Phase Retrieval
Ramina Ghods, Andrew Lan, Tom Goldstein, Christoph Studer ; PMLR 80:1729-1738
Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors
Soumya Ghosh, Jiayu Yao, Finale Doshi-Velez ; PMLR 80:1739-1748
Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial Time
Asish Ghoshal, Jean Honorio ; PMLR 80:1749-1757
Robust and Scalable Models of Microbiome Dynamics
Travis Gibson, Georg Gerber ; PMLR 80:1758-1767
Non-Linear Motor Control by Local Learning in Spiking Neural Networks
Aditya Gilra, Wulfram Gerstner ; PMLR 80:1768-1777
Learning One Convolutional Layer with Overlapping Patches
Surbhi Goel, Adam Klivans, Raghu Meka ; PMLR 80:1778-1786
Visualizing and Understanding Atari Agents
Samuel Greydanus, Anurag Koul, Jonathan Dodge, Alan Fern ; PMLR 80:1787-1796
Learning Policy Representations in Multiagent Systems
Aditya Grover, Maruan Al-Shedivat, Jayesh Gupta, Yuri Burda, Harrison Edwards ; PMLR 80:1797-1806
Faster Derivative-Free Stochastic Algorithm for Shared Memory Machines
Bin Gu, Zhouyuan Huo, Cheng Deng, Heng Huang ; PMLR 80:1807-1816
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Learning to Search with MCTSnets
Arthur Guez, Theophane Weber, Ioannis Antonoglou, Karen Simonyan, Oriol Vinyals, Daan Wierstra, Remi Munos, David Silver ; PMLR 80:1817-1826
Characterizing Implicit Bias in Terms of Optimization Geometry
Suriya Gunasekar, Jason Lee, Daniel Soudry, Nathan Srebro ; PMLR 80:1827-1836
Shampoo: Preconditioned Stochastic Tensor Optimization
Vineet Gupta, Tomer Koren, Yoram Singer ; PMLR 80:1837-1845
Latent Space Policies for Hierarchical Reinforcement Learning
Tuomas Haarnoja, Kristian Hartikainen, Pieter Abbeel, Sergey Levine ; PMLR 80:1846-1855
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, Sergey Levine ; PMLR 80:1856-1865
K-Beam Minimax: Efficient Optimization for Deep Adversarial Learning
Jihun Hamm, Yung-Kyun Noh ; PMLR 80:1876-1884
Candidates vs. Noises Estimation for Large Multi-Class Classification Problem
Lei Han, Yiheng Huang, Tong Zhang ; PMLR 80:1885-1894
Stein Variational Gradient Descent Without Gradient
Jun Han, Qiang Liu ; PMLR 80:1895-1903
Rectify Heterogeneous Models with Semantic Mapping
Ye Han-Jia, De-Chuan Zhan, Yuan Jiang, Zhi-Hua Zhou ; PMLR 80:1904-1913
Deep Models of Interactions Across Sets
Jason Hartford, Devon Graham, Kevin Leyton-Brown, Siamak Ravanbakhsh ; PMLR 80:1914-1923
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Learning Memory Access Patterns
Milad Hashemi, Kevin Swersky, Jamie Smith, Grant Ayers, Heiner Litz, Jichuan Chang, Christos Kozyrakis, Parthasarathy Ranganathan ; PMLR 80:1924-1933
Fairness Without Demographics in Repeated Loss Minimization
Tatsunori Hashimoto, Megha Srivastava, Hongseok Namkoong, Percy Liang ; PMLR 80:1934-1943
Multicalibration: Calibration for the (Computationally-Identifiable) Masses
Ursula Hebert-Johnson, Michael Kim, Omer Reingold, Guy Rothblum ; PMLR 80:1944-1953
Recurrent Predictive State Policy Networks
Ahmed Hefny, Zita Marinho, Wen Sun, Siddhartha Srinivasa, Geoffrey Gordon ; PMLR 80:1954-1963
Learning unknown ODE models with Gaussian processes
Markus Heinonen, Cagatay Yildiz, Henrik Mannerström, Jukka Intosalmi, Harri Lähdesmäki ; PMLR 80:1964-1973
Orthogonal Recurrent Neural Networks with Scaled Cayley Transform
Kyle Helfrich, Devin Willmott, Qiang Ye ; PMLR 80:1974-1983
Fast Bellman Updates for Robust MDPs
Chin Pang Ho, Marek Petrik, Wolfram Wiesemann ; PMLR 80:1984-1993
[ abs ] [ Download PDF ][ Supplementary PDF ]
CyCADA: Cycle-Consistent Adversarial Domain Adaptation
Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei Efros, Trevor Darrell ; PMLR 80:1994-2003
Sound Abstraction and Decomposition of Probabilistic Programs
Steven Holtzen, Guy Broeck, Todd Millstein ; PMLR 80:2004-2013
Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solution for Nonconvex Distributed Optimization Over Networks
Mingyi Hong, Meisam Razaviyayn, Jason Lee ; PMLR 80:2014-2023
Variational Bayesian dropout: pitfalls and fixes
Jiri Hron, Alexander G. G. Matthews, Zoubin Ghahramani ; PMLR 80:2024-2033
Does Distributionally Robust Supervised Learning Give Robust Classifiers?
Weihua Hu, Gang Niu, Issei Sato, Masashi Sugiyama ; PMLR 80:2034-2042
Dissipativity Theory for Accelerating Stochastic Variance Reduction: A Unified Analysis of SVRG and Katyusha Using Semidefinite Programs
Bin Hu, Stephen Wright, Laurent Lessard ; PMLR 80:2043-2052
Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices
Zengfeng Huang ; PMLR 80:2053-2062
Learning Deep ResNet Blocks Sequentially using Boosting Theory
Furong Huang, Jordan Ash, John Langford, Robert Schapire ; PMLR 80:2063-2072
Learning Hidden Markov Models from Pairwise Co-occurrences with Application to Topic Modeling
Kejun Huang, Xiao Fu, Nicholas Sidiropoulos ; PMLR 80:2073-2082
Decoupled Parallel Backpropagation with Convergence Guarantee
Zhouyuan Huo, Bin Gu, Yang, Heng Huang ; PMLR 80:2103-2111
Using Reward Machines for High-Level Task Specification and Decomposition in Reinforcement Learning
Rodrigo Toro Icarte, Toryn Klassen, Richard Valenzano, Sheila McIlraith ; PMLR 80:2112-2121
Deep Variational Reinforcement Learning for POMDPs
Maximilian Igl, Luisa Zintgraf, Tuan Anh Le, Frank Wood, Shimon Whiteson ; PMLR 80:2122-2131
Attention-based Deep Multiple Instance Learning
Maximilian Ilse, Jakub Tomczak, Max Welling ; PMLR 80:2132-2141
Black-box Adversarial Attacks with Limited Queries and Information
Andrew Ilyas, Logan Engstrom, Anish Athalye, Jessy Lin ; PMLR 80:2142-2151
Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model
Hideaki Imamura, Issei Sato, Masashi Sugiyama ; PMLR 80:2152-2161
Improving Regression Performance with Distributional Losses
Ehsan Imani, Martha White ; PMLR 80:2162-2171
Differentially Private Matrix Completion Revisited
Prateek Jain, Om Dipakbhai Thakkar, Abhradeep Thakurta ; PMLR 80:2220-2229
Video Prediction with Appearance and Motion Conditions
Yunseok Jang, Gunhee Kim, Yale Song ; PMLR 80:2230-2239
Pathwise Derivatives Beyond the Reparameterization Trick
Martin Jankowiak, Fritz Obermeyer ; PMLR 80:2240-2249
Detecting non-causal artifacts in multivariate linear regression models
Dominik Janzing, Bernhard Schölkopf ; PMLR 80:2250-2258
A Unified Framework for Structured Low-rank Matrix Learning
Pratik Jawanpuria, Bamdev Mishra ; PMLR 80:2259-2268
Efficient end-to-end learning for quantizable representations
Yeonwoo Jeong, Hyun Oh Song ; PMLR 80:2269-2278
Exploring Hidden Dimensions in Parallelizing Convolutional Neural Networks
Zhihao Jia, Sina Lin, Charles R. Qi, Alex Aiken ; PMLR 80:2279-2288
Feedback-Based Tree Search for Reinforcement Learning
Daniel Jiang, Emmanuel Ekwedike, Han Liu ; PMLR 80:2289-2298
Quickshift++: Provably Good Initializations for Sample-Based Mean Shift
Heinrich Jiang, Jennifer Jang, Samory Kpotufe ; PMLR 80:2299-2308
MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels
Lu Jiang, Zhengyuan Zhou, Thomas Leung, Li-Jia Li, Li Fei-Fei ; PMLR 80:2309-2318
The Weighted Kendall and High-order Kernels for Permutations
Yunlong Jiao, Jean-Philippe Vert ; PMLR 80:2319-2327
Junction Tree Variational Autoencoder for Molecular Graph Generation
Wengong Jin, Regina Barzilay, Tommi Jaakkola ; PMLR 80:2328-2337
Network Global Testing by Counting Graphlets
Jiashun Jin, Zheng Tracy Ke, Shengming Luo ; PMLR 80:2338-2346
Regret Minimization for Partially Observable Deep Reinforcement Learning
Peter Jin, Kurt Keutzer, Sergey Levine ; PMLR 80:2347-2356
WSNet: Compact and Efficient Networks Through Weight Sampling
Xiaojie Jin, Yingzhen Yang, Ning Xu, Jianchao Yang, Nebojsa Jojic, Jiashi Feng, Shuicheng Yan ; PMLR 80:2357-2366
Large-Scale Cox Process Inference using Variational Fourier Features
ST John, James Hensman ; PMLR 80:2367-2375
Composite Functional Gradient Learning of Generative Adversarial Models
Rie Johnson, Tong Zhang ; PMLR 80:2376-2384
Improving Sign Random Projections With Additional Information
Keegan Kang, Wong Wei Pin ; PMLR 80:2484-2492
Let’s be Honest: An Optimal No-Regret Framework for Zero-Sum Games
Ehsan Asadi Kangarshahi, Ya-Ping Hsieh, Mehmet Fatih Sahin, Volkan Cevher ; PMLR 80:2493-2501
Continual Reinforcement Learning with Complex Synapses
Christos Kaplanis, Murray Shanahan, Claudia Clopath ; PMLR 80:2502-2511
LaVAN: Localized and Visible Adversarial Noise
Danny Karmon, Daniel Zoran, Yoav Goldberg ; PMLR 80:2512-2520
Riemannian Stochastic Recursive Gradient Algorithm with Retraction and Vector Transport and Its Convergence Analysis
Hiroyuki Kasai, Hiroyuki Sato, Bamdev Mishra ; PMLR 80:2521-2529
Not All Samples Are Created Equal: Deep Learning with Importance Sampling
Angelos Katharopoulos, Francois Fleuret ; PMLR 80:2530-2539
Scalable Deletion-Robust Submodular Maximization: Data Summarization with Privacy and Fairness Constraints
Ehsan Kazemi, Morteza Zadimoghaddam, Amin Karbasi ; PMLR 80:2549-2558
[ abs ] [ Download PDF ][ Supplementary PDF ]
Focused Hierarchical RNNs for Conditional Sequence Processing
Nan Rosemary Ke, Konrad Żołna, Alessandro Sordoni, Zhouhan Lin, Adam Trischler, Yoshua Bengio, Joelle Pineau, Laurent Charlin, Christopher Pal ; PMLR 80:2559-2568
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
Michael Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu ; PMLR 80:2569-2577
Improved nearest neighbor search using auxiliary information and priority functions
Omid Keivani, Kaushik Sinha ; PMLR 80:2578-2586
ContextNet: Deep learning for Star Galaxy Classification
Noble Kennamer, David Kirkby, Alexander Ihler, Francisco Javier Sanchez-Lopez ; PMLR 80:2587-2595
Fast Maximization of Non-Submodular, Monotonic Functions on the Integer Lattice
Alan Kuhnle, J. David Smith, Victoria G.Crawford, My T. Thai ; PMLR 80:2791-2800
Accurate Uncertainties for Deep Learning Using Calibrated Regression
Volodymyr Kuleshov, Nathan Fenner, Stefano Ermon ; PMLR 80:2801-2809
Trainable Calibration Measures For Neural Networks From Kernel Mean Embeddings
Aviral Kumar, Sunita Sarawagi, Ujjwal Jain ; PMLR 80:2810-2819
Data-Dependent Stability of Stochastic Gradient Descent
Ilja Kuzborskij, Christoph Lampert ; PMLR 80:2820-2829
Explicit Inductive Bias for Transfer Learning with Convolutional Networks
Xuhong LI, Yves Grandvalet, Franck Davoine ; PMLR 80:2830-2839
Understanding the Loss Surface of Neural Networks for Binary Classification
SHIYU LIANG, Ruoyu Sun, Yixuan Li, Rayadurgam Srikant ; PMLR 80:2840-2849
Mixed batches and symmetric discriminators for GAN training
Thomas LUCAS, Corentin Tallec, Yann Ollivier, Jakob Verbeek ; PMLR 80:2850-2859
Binary Partitions with Approximate Minimum Impurity
Eduardo S. Laber, Marco Molinaro, Felipe A. Mello Pereira ; PMLR 80:2860-2868
Canonical Tensor Decomposition for Knowledge Base Completion
Timothee Lacroix, Nicolas Usunier, Guillaume Obozinski ; PMLR 80:2869-2878
Generalization without Systematicity: On the Compositional Skills of Sequence-to-Sequence Recurrent Networks
Brenden Lake, Marco Baroni ; PMLR 80:2879-2888
An Estimation and Analysis Framework for the Rasch Model
Andrew Lan, Mung Chiang, Christoph Studer ; PMLR 80:2889-2897
Partial Optimality and Fast Lower Bounds for Weighted Correlation Clustering
Jan-Hendrik Lange, Andreas Karrenbauer, Bjoern Andres ; PMLR 80:2898-2907
Deep Linear Networks with Arbitrary Loss: All Local Minima Are Global
Thomas Laurent, James Brecht ; PMLR 80:2908-2913
The Multilinear Structure of ReLU Networks
Thomas Laurent, James Brecht ; PMLR 80:2914-2922
Hierarchical Imitation and Reinforcement Learning
Hoang Le, Nan Jiang, Alekh Agarwal, Miroslav Dudik, Yisong Yue, Hal Daumé ; PMLR 80:2923-2932
Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace
Yoonho Lee, Seungjin Choi ; PMLR 80:2933-2942
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 ; PMLR 80:2943-2952
Deep Asymmetric Multi-task Feature Learning
Hae Beom Lee, Eunho Yang, Sung Ju Hwang ; PMLR 80:2962-2970
Noise2Noise: Learning Image Restoration without Clean Data
Jaakko Lehtinen, Jacob Munkberg, Jon Hasselgren, Samuli Laine, Tero Karras, Miika Aittala, Timo Aila ; PMLR 80:2971-2980
Out-of-sample extension of graph adjacency spectral embedding
Keith Levin, Farbod Roosta-Khorasani, Michael W. Mahoney, Carey E. Priebe ; PMLR 80:2981-2990
An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks
Qianxiao Li, Shuji Hao ; PMLR 80:2991-3000
Towards Binary-Valued Gates for Robust LSTM Training
Zhuohan Li, Di He, Fei Tian, Wei Chen, Tao Qin, Liwei Wang, Tie-Yan Liu ; PMLR 80:3001-3010
On the Limitations of First-Order Approximation in GAN Dynamics
Jerry Li, Aleksander Madry, John Peebles, Ludwig Schmidt ; PMLR 80:3011-3019
Submodular Hypergraphs: p-Laplacians, Cheeger Inequalities and Spectral Clustering
Pan Li, Olgica Milenkovic ; PMLR 80:3020-3029
Estimation of Markov Chain via Rank-constrained Likelihood
Xudong Li, Mengdi Wang, Anru Zhang ; PMLR 80:3039-3048
Asynchronous Decentralized Parallel Stochastic Gradient Descent
Xiangru Lian, Wei Zhang, Ce Zhang, Ji Liu ; PMLR 80:3049-3058
[ abs ] [ Download PDF ][ Supplementary PDF ]
RLlib: Abstractions for Distributed Reinforcement Learning
Eric Liang, Richard Liaw, Robert Nishihara, Philipp Moritz, Roy Fox, Ken Goldberg, Joseph Gonzalez, Michael Jordan, Ion Stoica ; PMLR 80:3059-3068
On the Spectrum of Random Features Maps of High Dimensional Data
Zhenyu Liao, Romain Couillet ; PMLR 80:3069-3077
The Dynamics of Learning: A Random Matrix Approach
Zhenyu Liao, Romain Couillet ; PMLR 80:3078-3087
[ abs ] [ Download PDF ][ Supplementary PDF ]
Reviving and Improving Recurrent Back-Propagation
Renjie Liao, Yuwen Xiong, Ethan Fetaya, Lisa Zhang, KiJung Yoon, Xaq Pitkow, Raquel Urtasun, Richard Zemel ; PMLR 80:3088-3097
Optimal Distributed Learning with Multi-pass Stochastic Gradient Methods
Junhong Lin, Volkan Cevher ; PMLR 80:3098-3107
Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces
Junhong Lin, Volkan Cevher ; PMLR 80:3108-3117
Level-Set Methods for Finite-Sum Constrained Convex Optimization
Qihang Lin, Runchao Ma, Tianbao Yang ; PMLR 80:3118-3127
Detecting and Correcting for Label Shift with Black Box Predictors
Zachary Lipton, Yu-Xiang Wang, Alexander Smola ; PMLR 80:3128-3136
Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression
Haitao Liu, Jianfei Cai, Yi Wang, Yew Soon Ong ; PMLR 80:3137-3146
Towards Black-box Iterative Machine Teaching
Weiyang Liu, Bo Dai, Xingguo Li, Zhen Liu, James Rehg, Le Song ; PMLR 80:3147-3155
Delayed Impact of Fair Machine Learning
Lydia Liu, Sarah Dean, Esther Rolf, Max Simchowitz, Moritz Hardt ; PMLR 80:3156-3164
A Two-Step Computation of the Exact GAN Wasserstein Distance
Huidong Liu, Xianfeng GU, Dimitris Samaras ; PMLR 80:3165-3174
Open Category Detection with PAC Guarantees
Si Liu, Risheek Garrepalli, Thomas Dietterich, Alan Fern, Dan Hendrycks ; PMLR 80:3175-3184
Fast Variance Reduction Method with Stochastic Batch Size
Xuanqing Liu, Cho-Jui Hsieh ; PMLR 80:3185-3194
Fast Stochastic AUC Maximization with O(1/n)-Convergence Rate
Mingrui Liu, Xiaoxuan Zhang, Zaiyi Chen, Xiaoyu Wang, Tianbao Yang ; PMLR 80:3195-3203
On Matching Pursuit and Coordinate Descent
Francesco Locatello, Anant Raj, Sai Praneeth Karimireddy, Gunnar Raetsch, Bernhard Schölkopf, Sebastian Stich, Martin Jaggi ; PMLR 80:3204-3213
PDE-Net: Learning PDEs from Data
Zichao Long, Yiping Lu, Xianzhong Ma, Bin Dong ; PMLR 80:3214-3222
Error Estimation for Randomized Least-Squares Algorithms via the Bootstrap
Miles Lopes, Shusen Wang, Michael Mahoney ; PMLR 80:3223-3232
Constraining the Dynamics of Deep Probabilistic Models
Marco Lorenzi, Maurizio Filippone ; PMLR 80:3233-3242
Spectrally Approximating Large Graphs with Smaller Graphs
Andreas Loukas, Pierre Vandergheynst ; PMLR 80:3243-3252
The Edge Density Barrier: Computational-Statistical Tradeoffs in Combinatorial Inference
Hao Lu, Yuan Cao, Junwei Lu, Han Liu, Zhaoran Wang ; PMLR 80:3253-3262
Accelerating Greedy Coordinate Descent Methods
Haihao Lu, Robert Freund, Vahab Mirrokni ; PMLR 80:3263-3272
Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations
Yiping Lu, Aoxiao Zhong, Quanzheng Li, Bin Dong ; PMLR 80:3282-3291
End-to-end Active Object Tracking via Reinforcement Learning
Wenhan Luo, Peng Sun, Fangwei Zhong, Wei Liu, Tong Zhang, Yizhou Wang ; PMLR 80:3292-3301
Competitive Caching with Machine Learned Advice
Thodoris Lykouris, Sergei Vassilvtiskii ; PMLR 80:3302-3311
Batch Bayesian Optimization via Multi-objective Acquisition Ensemble for Automated Analog Circuit Design
Wenlong Lyu, Fan Yang, Changhao Yan, Dian Zhou, Xuan Zeng ; PMLR 80:3312-3320
Celer: a Fast Solver for the Lasso with Dual Extrapolation
Mathurin MASSIAS, Joseph Salmon, Alexandre Gramfort ; PMLR 80:3321-3330
The Power of Interpolation: Understanding the Effectiveness of SGD in Modern Over-parametrized Learning
Siyuan Ma, Raef Bassily, Mikhail Belkin ; PMLR 80:3331-3340
Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers
Yao Ma, Alexander Olshevsky, Csaba Szepesvari, Venkatesh Saligrama ; PMLR 80:3341-3350
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval and Matrix Completion
Cong Ma, Kaizheng Wang, Yuejie Chi, Yuxin Chen ; PMLR 80:3351-3360
[ abs ] [ Download PDF ][ Supplementary PDF ]
Dimensionality-Driven Learning with Noisy Labels
Xingjun Ma, Yisen Wang, Michael E. Houle, Shuo Zhou, Sarah M. Erfani, Shu-Tao Xia, Sudanthi Wijewickrema, James Bailey ; PMLR 80:3361-3370
Approximate message passing for amplitude based optimization
Junjie Ma, Ji Xu, Arian Maleki ; PMLR 80:3371-3380
Learning Adversarially Fair and Transferable Representations
David Madras, Elliot Creager, Toniann Pitassi, Richard Zemel ; PMLR 80:3381-3390
An Efficient, Generalized Bellman Update For Cooperative Inverse Reinforcement Learning
Dhruv Malik, Malayandi Palaniappan, Jaime Fisac, Dylan Hadfield-Menell, Stuart Russell, Anca Dragan ; PMLR 80:3391-3399
Streaming Principal Component Analysis in Noisy Settings
Teodor Vanislavov Marinov, Poorya Mianjy, Raman Arora ; PMLR 80:3410-3419
Fast Approximate Spectral Clustering for Dynamic Networks
Lionel Martin, Andreas Loukas, Pierre Vandergheynst ; PMLR 80:3420-3429
Bayesian Model Selection for Change Point Detection and Clustering
Othmane Mazhar, Cristian Rojas, Carlo Fischione, Mohammad Reza Hesamzadeh ; PMLR 80:3430-3439
Optimization, Fast and Slow: Optimally Switching between Local and Bayesian Optimization
Mark McLeod, Stephen Roberts, Michael A. Osborne ; PMLR 80:3440-3449
Bounds on the Approximation Power of Feedforward Neural Networks
Mohammad Mehrabi, Aslan Tchamkerten, MANSOOR YOUSEFI ; PMLR 80:3450-3458
Differentiable Dynamic Programming for Structured Prediction and Attention
Arthur Mensch, Mathieu Blondel ; PMLR 80:3459-3468
Ranking Distributions based on Noisy Sorting
Adil El Mesaoudi-Paul, Eyke Hüllermeier, Robert Busa-Fekete ; PMLR 80:3469-3477
Which Training Methods for GANs do actually Converge?
Lars Mescheder, Andreas Geiger, Sebastian Nowozin ; PMLR 80:3478-3487
Differentiable plasticity: training plastic neural networks with backpropagation
Thomas Miconi, Kenneth Stanley, Jeff Clune ; PMLR 80:3556-3565
Training Neural Machines with Trace-Based Supervision
Matthew Mirman, Dimitar Dimitrov, Pavle Djordjevic, Timon Gehr, Martin Vechev ; PMLR 80:3566-3574
Differentiable Abstract Interpretation for Provably Robust Neural Networks
Matthew Mirman, Timon Gehr, Martin Vechev ; PMLR 80:3575-3583
A Delay-tolerant Proximal-Gradient Algorithm for Distributed Learning
Konstantin Mishchenko, Franck Iutzeler, Jérôme Malick, Massih-Reza Amini ; PMLR 80:3584-3592
Data Summarization at Scale: A Two-Stage Submodular Approach
Marko Mitrovic, Ehsan Kazemi, Morteza Zadimoghaddam, Amin Karbasi ; PMLR 80:3593-3602
The Hierarchical Adaptive Forgetting Variational Filter
Vincent Moens ; PMLR 80:3603-3612
Decentralized Submodular Maximization: Bridging Discrete and Continuous Settings
Aryan Mokhtari, Hamed Hassani, Amin Karbasi ; PMLR 80:3613-3622
DICOD: Distributed Convolutional Coordinate Descent for Convolutional Sparse Coding
Thomas Moreau, Laurent Oudre, Nicolas Vayatis ; PMLR 80:3623-3631
WHInter: A Working set algorithm for High-dimensional sparse second order Interaction models.
Marine Le Morvan, Jean-Philippe Vert ; PMLR 80:3632-3641
Dropout Training, Data-dependent Regularization, and Generalization Bounds
Wenlong Mou, Yuchen Zhou, Jun Gao, Liwei Wang ; PMLR 80:3642-3650
Rapid Adaptation with Conditionally Shifted Neurons
Tsendsuren Munkhdalai, Xingdi Yuan, Soroush Mehri, Adam Trischler ; PMLR 80:3661-3670
On the Relationship between Data Efficiency and Error for Uncertainty Sampling
Stephen Mussmann, Percy Liang ; PMLR 80:3671-3679
Fitting New Speakers Based on a Short Untranscribed Sample
Eliya Nachmani, Adam Polyak, Yaniv Taigman, Lior Wolf ; PMLR 80:3680-3688
Smoothed Action Value Functions for Learning Gaussian Policies
Ofir Nachum, Mohammad Norouzi, George Tucker, Dale Schuurmans ; PMLR 80:3689-3697
Nearly Optimal Robust Subspace Tracking
Praneeth Narayanamurthy, Namrata Vaswani ; PMLR 80:3698-3706
Stochastic Proximal Algorithms for AUC Maximization
Michael Natole, Yiming Ying, Siwei Lyu ; PMLR 80:3707-3716
Mitigating Bias in Adaptive Data Gathering via Differential Privacy
Seth Neel, Aaron Roth ; PMLR 80:3717-3726
Optimization Landscape and Expressivity of Deep CNNs
Quynh Nguyen, Matthias Hein ; PMLR 80:3727-3736
Neural Networks Should Be Wide Enough to Learn Disconnected Decision Regions
Quynh Nguyen, Mahesh Chandra Mukkamala, Matthias Hein ; PMLR 80:3737-3746
SGD and Hogwild! Convergence Without the Bounded Gradients Assumption
Lam Nguyen, PHUONG HA NGUYEN, Marten Dijk, Peter Richtarik, Katya Scheinberg, Martin Takac ; PMLR 80:3747-3755
Active Testing: An Efficient and Robust Framework for Estimating Accuracy
Phuc Nguyen, Deva Ramanan, Charless Fowlkes ; PMLR 80:3756-3765
On Learning Sparsely Used Dictionaries from Incomplete Samples
Thanh Nguyen, Akshay Soni, Chinmay Hegde ; PMLR 80:3766-3775
Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry
Maximillian Nickel, Douwe Kiela ; PMLR 80:3776-3785
State Space Gaussian Processes with Non-Gaussian Likelihood
Hannes Nickisch, Arno Solin, Alexander Grigorevskiy ; PMLR 80:3786-3795
SparseMAP: Differentiable Sparse Structured Inference
Vlad Niculae, Andre Martins, Mathieu Blondel, Claire Cardie ; PMLR 80:3796-3805
A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations
Weili Nie, Yang Zhang, Ankit Patel ; PMLR 80:3806-3815
Functional Gradient Boosting based on Residual Network Perception
Atsushi Nitanda, Taiji Suzuki ; PMLR 80:3816-3825
Beyond 1/2-Approximation for Submodular Maximization on Massive Data Streams
Ashkan Norouzi-Fard, Jakub Tarnawski, Slobodan Mitrovic, Amir Zandieh, Aidasadat Mousavifar, Ola Svensson ; PMLR 80:3826-3835
The Uncertainty Bellman Equation and Exploration
Brendan O’Donoghue, Ian Osband, Remi Munos, Volodymyr Mnih ; PMLR 80:3836-3845
Is Generator Conditioning Causally Related to GAN Performance?
Augustus Odena, Jacob Buckman, Catherine Olsson, Tom Brown, Christopher Olah, Colin Raffel, Ian Goodfellow ; PMLR 80:3846-3855
Learning in Reproducing Kernel Krein Spaces
Dino Oglic, Thomas Gaertner ; PMLR 80:3856-3864
BOCK : Bayesian Optimization with Cylindrical Kernels
ChangYong Oh, Efstratios Gavves, Max Welling ; PMLR 80:3865-3874
Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches
Simon Olofsson, Marc Deisenroth, Ruth Misener ; PMLR 80:3905-3914
[ abs ] [ Download PDF ][ Supplementary PDF ]
Parallel WaveNet: Fast High-Fidelity Speech Synthesis
Aaron Oord, Yazhe Li, Igor Babuschkin, Karen Simonyan, Oriol Vinyals, Koray Kavukcuoglu, George Driessche, Edward Lockhart, Luis Cobo, Florian Stimberg, Norman Casagrande, Dominik Grewe, Seb Noury, Sander Dieleman, Erich Elsen, Nal Kalchbrenner, Heiga Zen, Alex Graves, Helen King, Tom Walters, Dan Belov, Demis Hassabis ; PMLR 80:3915-3923
Learning Localized Spatio-Temporal Models From Streaming Data
Muhammad Osama, Dave Zachariah, Thomas Schön ; PMLR 80:3924-3932
Autoregressive Quantile Networks for Generative Modeling
Georg Ostrovski, Will Dabney, Remi Munos ; PMLR 80:3933-3942
Efficient First-Order Algorithms for Adaptive Signal Denoising
Dmitrii Ostrovskii, Zaid Harchaoui ; PMLR 80:3943-3952
Analyzing Uncertainty in Neural Machine Translation
Myle Ott, Michael Auli, David Grangier, Marc’Aurelio Ranzato ; PMLR 80:3953-3962
Learning Compact Neural Networks with Regularization
Samet Oymak ; PMLR 80:3963-3972
Tree Edit Distance Learning via Adaptive Symbol Embeddings
Benjamin Paaßen, Claudio Gallicchio, Alessio Micheli, Barbara Hammer ; PMLR 80:3973-3982
Reinforcement Learning with Function-Valued Action Spaces for Partial Differential Equation Control
Yangchen Pan, Amir-massoud Farahmand, Martha White, Saleh Nabi, Piyush Grover, Daniel Nikovski ; PMLR 80:3983-3992
Learning to Speed Up Structured Output Prediction
Xingyuan Pan, Vivek Srikumar ; PMLR 80:3993-4002
Theoretical Analysis of Image-to-Image Translation with Adversarial Learning
Xudong Pan, Mi Zhang, Daizong Ding ; PMLR 80:4003-4012
Max-Mahalanobis Linear Discriminant Analysis Networks
Tianyu Pang, Chao Du, Jun Zhu ; PMLR 80:4013-4022
Efficient Neural Architecture Search via Parameter Sharing
Hieu Pham, Melody Guan, Barret Zoph, Quoc Le, Jeff Dean ; PMLR 80:4092-4101
Bandits with Delayed, Aggregated Anonymous Feedback
Ciara Pike-Burke, Shipra Agrawal, Csaba Szepesvari, Steffen Grunewalder ; PMLR 80:4102-4110
Constant-Time Predictive Distributions for Gaussian Processes
Geoff Pleiss, Jacob Gardner, Kilian Weinberger, Andrew Gordon Wilson ; PMLR 80:4111-4120
Local Convergence Properties of SAGA/Prox-SVRG and Acceleration
Clarice Poon, Jingwei Liang, Carola-Bibiane Schoenlieb ; PMLR 80:4121-4129
Equivalence of Multicategory SVM and Simplex Cone SVM: Fast Computations and Statistical Theory
Guillaume Pouliot ; PMLR 80:4130-4137
Learning Dynamics of Linear Denoising Autoencoders
Arnu Pretorius, Steve Kroon, Herman Kamper ; PMLR 80:4138-4147
[ abs ] [ Download PDF ][ Supplementary PDF ]
JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets
Yunchen Pu, Shuyang Dai, Zhe Gan, Weiyao Wang, Guoyin Wang, Yizhe Zhang, Ricardo Henao, Lawrence Carin Duke ; PMLR 80:4148-4157
Selecting Representative Examples for Program Synthesis
Yewen Pu, Zachery Miranda, Armando Solar-Lezama, Leslie Kaelbling ; PMLR 80:4158-4167
Generalized Earley Parser: Bridging Symbolic Grammars and Sequence Data for Future Prediction
Siyuan Qi, Baoxiong Jia, Song-Chun Zhu ; PMLR 80:4168-4176
Fast Parametric Learning with Activation Memorization
Jack Rae, Chris Dyer, Peter Dayan, Timothy Lillicrap ; PMLR 80:4225-4234
Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games?
Maithra Raghu, Alex Irpan, Jacob Andreas, Robert Kleinberg, Quoc Le, Jon Kleinberg ; PMLR 80:4235-4243
Cut-Pursuit Algorithm for Regularizing Nonsmooth Functionals with Graph Total Variation
Hugo Raguet, Loic Landrieu ; PMLR 80:4244-4253
Modeling Others using Oneself in Multi-Agent Reinforcement Learning
Roberta Raileanu, Emily Denton, Arthur Szlam, Rob Fergus ; PMLR 80:4254-4263
On Nesting Monte Carlo Estimators
Tom Rainforth, Robert Cornish, Hongseok Yang, Andrew Warrington ; PMLR 80:4264-4273
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Tighter Variational Bounds are Not Necessarily Better
Tom Rainforth, Adam Kosiorek, Tuan Anh Le, Chris Maddison, Maximilian Igl, Frank Wood, Yee Whye Teh ; PMLR 80:4274-4282
SAFFRON: an Adaptive Algorithm for Online Control of the False Discovery Rate
Aaditya Ramdas, Tijana Zrnic, Martin Wainwright, Michael Jordan ; PMLR 80:4283-4291
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
Tabish Rashid, Mikayel Samvelyan, Christian Schroeder Witt, Gregory Farquhar, Jakob Foerster, Shimon Whiteson ; PMLR 80:4292-4301
Gradient Coding from Cyclic MDS Codes and Expander Graphs
Netanel Raviv, Rashish Tandon, Alex Dimakis, Itzhak Tamo ; PMLR 80:4302-4310
Learning Implicit Generative Models with the Method of Learned Moments
Suman Ravuri, Shakir Mohamed, Mihaela Rosca, Oriol Vinyals ; PMLR 80:4311-4320
Weightless: Lossy weight encoding for deep neural network compression
Brandon Reagan, Udit Gupta, Bob Adolf, Michael Mitzenmacher, Alexander Rush, Gu-Yeon Wei, David Brooks ; PMLR 80:4321-4330
Learning to Reweight Examples for Robust Deep Learning
Mengye Ren, Wenyuan Zeng, Bin Yang, Raquel Urtasun ; PMLR 80:4331-4340
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Learning by Playing Solving Sparse Reward Tasks from Scratch
Martin Riedmiller, Roland Hafner, Thomas Lampe, Michael Neunert, Jonas Degrave, Tom Wiele, Vlad Mnih, Nicolas Heess, Jost Tobias Springenberg ; PMLR 80:4341-4350
Been There, Done That: Meta-Learning with Episodic Recall
Samuel Ritter, Jane Wang, Zeb Kurth-Nelson, Siddhant Jayakumar, Charles Blundell, Razvan Pascanu, Matthew Botvinick ; PMLR 80:4351-4360
A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music
Adam Roberts, Je