NeurIPS 2019

1,428 papers

Preference-Based Batch and Sequential Teaching: Towards a Unified View of Models

Farnam Mansouri, Yuxin Chen, Ara Vartanian, Xiaojin Zhu, Adish Singla

Extending Stein's unbiased risk estimator to train deep denoisers with correlated pairs of noisy images

Magauiya Zhussip, Shakarim Soltanayev, Se Young Chun

Beating SGD Saturation with Tail-Averaging and Minibatching

Nicole Muecke, Gergely Neu, Lorenzo Rosasco

A Prior of a Googol Gaussians: a Tensor Ring Induced Prior for Generative Models

Maxim Kuznetsov, Daniil Polykovskiy, Dmitry P Vetrov, Alex Zhebrak

Superposition of many models into one

Brian Cheung, Alexander Terekhov, Yubei Chen, Pulkit Agrawal, Bruno Olshausen

Understanding Attention and Generalization in Graph Neural Networks

Boris Knyazev, Graham W. Taylor, Mohamed Amer

Large-scale optimal transport map estimation using projection pursuit

Cheng Meng, Yuan Ke, Jingyi Zhang, Mengrui Zhang, Wenxuan Zhong

Towards modular and programmable architecture search

Renato Negrinho, Matthew Gormley, Geoffrey J. Gordon, Darshan Patil, Nghia Le

Communication trade-offs for Local-SGD with large step size

Aymeric Dieuleveut, Kumar Kshitij Patel

A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation

Runzhe Yang, Xingyuan Sun, Karthik Narasimhan

A Generic Acceleration Framework for Stochastic Composite Optimization

Andrei Kulunchakov, Julien Mairal

Learning Robust Options by Conditional Value at Risk Optimization

Takuya Hiraoka, Takahisa Imagawa, Tatsuya Mori, Takashi Onishi, Yoshimasa Tsuruoka

Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning

Enrique Fita Sanmartin, Sebastian Damrich, Fred A. Hamprecht

Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks

Yaqin Zhou, Shangqing Liu, Jingkai Siow, Xiaoning Du, Yang Liu

AGEM: Solving Linear Inverse Problems via Deep Priors and Sampling

Bichuan Guo, Yuxing Han, Jiangtao Wen

Non-asymptotic Analysis of Stochastic Methods for Non-Smooth Non-Convex Regularized Problems

Yi Xu, Rong Jin, Tianbao Yang

Tight Sample Complexity of Learning One-hidden-layer Convolutional Neural Networks

Yuan Cao, Quanquan Gu

Covariate-Powered Empirical Bayes Estimation

Nikolaos Ignatiadis, Stefan Wager

A Kernel Loss for Solving the Bellman Equation

Yihao Feng, Lihong Li, Qiang Liu

Can Unconditional Language Models Recover Arbitrary Sentences?

Nishant Subramani, Samuel Bowman, Kyunghyun Cho

vGraph: A Generative Model for Joint Community Detection and Node Representation Learning

Fan-Yun Sun, Meng Qu, Jordan Hoffmann, Chin-Wei Huang, Jian Tang

Explicit Planning for Efficient Exploration in Reinforcement Learning

Liangpeng Zhang, Ke Tang, Xin Yao

Model Similarity Mitigates Test Set Overuse

Horia Mania, John Miller, Ludwig Schmidt, Moritz Hardt, Benjamin Recht

Large Scale Structure of Neural Network Loss Landscapes

Stanislav Fort, Stanislaw Jastrzebski

Generalization in Generative Adversarial Networks: A Novel Perspective from Privacy Protection

Bingzhe Wu, Shiwan Zhao, Chaochao Chen, Haoyang Xu, Li Wang

Graph Agreement Models for Semi-Supervised Learning

Otilia Stretcu, Krishnamurthy Viswanathan, Dana Movshovitz-Attias, Emmanouil Platanios, Sujith Ravi

Fast and Accurate Least-Mean-Squares Solvers

Alaa Maalouf, Ibrahim Jubran, Dan Feldman

Provably Efficient Q-Learning with Low Switching Cost

Yu Bai, Tengyang Xie, Nan Jiang, Yu-Xiang Wang

Drill-down: Interactive Retrieval of Complex Scenes using Natural Language Queries

Fuwen Tan, Paola Cascante-Bonilla, Xiaoxiao Guo, Hui Wu, Song Feng

Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness

Saeed Mahloujifar, Xiao Zhang, Mohammad Mahmoody, David Evans

A Model to Search for Synthesizable Molecules

John Bradshaw, Brooks Paige, Matt J Kusner, Marwin Segler, José Miguel Hernández-Lobato

Fast, Provably convergent IRLS Algorithm for p-norm Linear Regression

Deeksha Adil, Richard Peng, Sushant Sachdeva

Limitations of the empirical Fisher approximation for natural gradient descent

Frederik Kunstner, Philipp Hennig, Lukas Balles

Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets

Rohith Kuditipudi, Xiang Wang, Holden Lee, Yi Zhang, Zhiyuan Li

General Proximal Incremental Aggregated Gradient Algorithms: Better and Novel Results under General Scheme

Tao Sun, Yuejiao Sun, Dongsheng Li, Qing Liao

Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models

Vincent LE GUEN, Nicolas Thome

Regularized Gradient Boosting

Corinna Cortes, Mehryar Mohri, Dmitry Storcheus

Learning nonlinear level sets for dimensionality reduction in function approximation

Guannan Zhang, Jiaxin Zhang, Jacob Hinkle

On the convergence of single-call stochastic extra-gradient methods

Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos

General E(2)-Equivariant Steerable CNNs

Maurice Weiler, Gabriele Cesa

Stochastic Continuous Greedy ++: When Upper and Lower Bounds Match

Amin Karbasi, Hamed Hassani, Aryan Mokhtari, Zebang Shen

GRU-ODE-Bayes: Continuous Modeling of Sporadically-Observed Time Series

Edward De Brouwer, Jaak Simm, Adam Arany, Yves Moreau

Fast and Provable ADMM for Learning with Generative Priors

Fabian Latorre, Armin eftekhari, Volkan Cevher

Glyce: Glyph-vectors for Chinese Character Representations

Yuxian Meng, Wei Wu, Fei Wang, Xiaoya Li, Ping Nie

Worst-Case Regret Bounds for Exploration via Randomized Value Functions

Daniel Russo

PC-Fairness: A Unified Framework for Measuring Causality-based Fairness

Yongkai Wu, Lu Zhang, Xintao Wu, Hanghang Tong

SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems

Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael

Invariance and identifiability issues for word embeddings

Rachel Carrington, Karthik Bharath, Simon Preston

FreeAnchor: Learning to Match Anchors for Visual Object Detection

Xiaosong Zhang, Fang Wan, Chang Liu, Rongrong Ji, Qixiang Ye

Learning-In-The-Loop Optimization: End-To-End Control And Co-Design Of Soft Robots Through Learned Deep Latent Representations

Andrew Spielberg, Allan Zhao, Yuanming Hu, Tao Du, Wojciech Matusik

Model Selection for Contextual Bandits

Dylan J Foster, Akshay Krishnamurthy, Haipeng Luo

A New Distribution on the Simplex with Auto-Encoding Applications

Andrew Stirn, Tony Jebara, David Knowles

On the Correctness and Sample Complexity of Inverse Reinforcement Learning

Abi Komanduru, Jean Honorio

Latent Ordinary Differential Equations for Irregularly-Sampled Time Series

Yulia Rubanova, Ricky T. Q. Chen, David K. Duvenaud

Graph-Based Semi-Supervised Learning with Non-ignorable Non-response

Fan Zhou, Tengfei Li, Haibo Zhou, Hongtu Zhu, Ye Jieping

Provably robust boosted decision stumps and trees against adversarial attacks

Maksym Andriushchenko, Matthias Hein

Facility Location Problem in Differential Privacy Model Revisited

Yunus Esencayi, Marco Gaboardi, Shi Li, Di Wang

PHYRE: A New Benchmark for Physical Reasoning

Anton Bakhtin, Laurens van der Maaten, Justin Johnson, Laura Gustafson, Ross Girshick

Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual Recognition

Satoshi Tsutsui, Yanwei Fu, David Crandall

Differentially Private Covariance Estimation

Kareem Amin, Travis Dick, Alex Kulesza, Andres Munoz, Sergei Vassilvitskii

DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation

Shashank Rajput, Hongyi Wang, Zachary Charles, Dimitris Papailiopoulos

Bootstrapping Upper Confidence Bound

Botao Hao, Yasin Abbasi Yadkori, Zheng Wen, Guang Cheng

Information-Theoretic Confidence Bounds for Reinforcement Learning

Xiuyuan Lu, Benjamin Van Roy

Modelling the Dynamics of Multiagent Q-Learning in Repeated Symmetric Games: a Mean Field Theoretic Approach

Shuyue Hu, Chin-wing Leung, Ho-fung Leung

Faster Boosting with Smaller Memory

Julaiti Alafate, Yoav S Freund

Online Markov Decoding: Lower Bounds and Near-Optimal Approximation Algorithms

Vikas Garg, Tamar Pichkhadze

First-order methods almost always avoid saddle points: The case of vanishing step-sizes

Ioannis Panageas, Georgios Piliouras, Xiao Wang

Dynamic Ensemble Modeling Approach to Nonstationary Neural Decoding in Brain-Computer Interfaces

Yu Qi, Bin Liu, Yueming Wang, Gang Pan

A Regularized Approach to Sparse Optimal Policy in Reinforcement Learning

Wenhao Yang, Xiang Li, Zhihua Zhang

Average-Case Averages: Private Algorithms for Smooth Sensitivity and Mean Estimation

Mark Bun, Thomas Steinke

Learning Robust Global Representations by Penalizing Local Predictive Power

Haohan Wang, Songwei Ge, Zachary Lipton, Eric P. Xing, Eric P Xing

A Self Validation Network for Object-Level Human Attention Estimation

Zehua Zhang, Chen Yu, David Crandall

Robust and Communication-Efficient Collaborative Learning

Amirhossein Reisizadeh, Hossein Taheri, Aryan Mokhtari, Hamed Hassani, Ramtin Pedarsani

Defending Against Neural Fake News

Rowan Zellers, Ari Holtzman, Hannah Rashkin, Yonatan Bisk, Ali Farhadi

Censored Semi-Bandits: A Framework for Resource Allocation with Censored Feedback

Arun Verma, Manjesh Hanawal, Arun Rajkumar, Raman Sankaran

Learning Positive Functions with Pseudo Mirror Descent

Yingxiang Yang, Haoxiang Wang, Negar Kiyavash, Niao He

Revisiting the Bethe-Hessian: Improved Community Detection in Sparse Heterogeneous Graphs

Lorenzo Dall'Amico, Romain Couillet, Nicolas Tremblay

PAC-Bayes Un-Expected Bernstein Inequality

Zakaria Mhammedi, Peter Grünwald, Benjamin Guedj

Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints

Sebastian Tschiatschek, Ahana Ghosh, Luis Haug, Rati Devidze, Adish Singla

Provably Efficient Q-learning with Function Approximation via Distribution Shift Error Checking Oracle

Simon S Du, Yuping Luo, Ruosong Wang, Hanrui Zhang

More Is Less: Learning Efficient Video Representations by Big-Little Network and Depthwise Temporal Aggregation

Quanfu Fan, Chun-Fu (Richard) Chen, Hilde Kuehne, Marco Pistoia, David Cox

A Geometric Perspective on Optimal Representations for Reinforcement Learning

Marc Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taiga, Pablo Samuel Castro

Stability of Graph Scattering Transforms

Fernando Gama, Alejandro Ribeiro, Joan Bruna

Optimizing Generalized Rate Metrics with Three Players

Harikrishna Narasimhan, Andrew Cotter, Maya Gupta

On Fenchel Mini-Max Learning

Chenyang Tao, Liqun Chen, Shuyang Dai, Junya Chen, Ke Bai

Differentially Private Distributed Data Summarization under Covariate Shift

Kanthi Sarpatwar, Karthikeyan Shanmugam, Venkata Sitaramagiridharganesh Ganapavarapu, Ashish Jagmohan, Roman Vaculin

Interaction Hard Thresholding: Consistent Sparse Quadratic Regression in Sub-quadratic Time and Space

Shuo Yang, Yanyao Shen, Sujay Sanghavi

A Structured Prediction Approach for Generalization in Cooperative Multi-Agent Reinforcement Learning

Nicolas Carion, Nicolas Usunier, Gabriel Synnaeve, Alessandro Lazaric

Complexity of Highly Parallel Non-Smooth Convex Optimization

Sébastien Bubeck, Qijia Jiang, Yuanzhi Li, Aaron Sidford, Yin Tat Lee

Private Stochastic Convex Optimization with Optimal Rates

Raef Bassily, Vitaly Feldman, Kunal Talwar, Abhradeep Guha Thakurta

Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG

Yujia Jin, Aaron Sidford

Convergence-Rate-Matching Discretization of Accelerated Optimization Flows Through Opportunistic State-Triggered Control

Miguel Vaquero, Jorge Cortes

Mapping State Space using Landmarks for Universal Goal Reaching

Zhiao Huang, Fangchen Liu, Hao Su

Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments

Vasilis Syrgkanis, Victor Lei, Miruna Oprescu, Maggie Hei, Keith Battocchi

Compacting, Picking and Growing for Unforgetting Continual Learning

Ching-Yi Hung, Cheng-Hao Tu, Cheng-En Wu, Chien-Hung Chen, Yi-Ming Chan

CXPlain: Causal Explanations for Model Interpretation under Uncertainty

Patrick Schwab, Walter Karlen

Efficient Neural Architecture Transformation Search in Channel-Level for Object Detection

Junran Peng, Ming Sun, ZHAO-XIANG ZHANG, Tieniu Tan, Junjie Yan

Regression Planning Networks

Danfei Xu, Roberto Martín-Martín, De-An Huang, Yuke Zhu, Silvio Savarese

Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers

Hadi Salman, Jerry Li, Ilya Razenshteyn, Pengchuan Zhang, Huan Zhang

PAC-Bayes under potentially heavy tails

Matthew Holland