NeurIPS 2019

1,428 papers

Near-Optimal Reinforcement Learning in Dynamic Treatment Regimes

Junzhe Zhang, Elias Bareinboim

Chasing Ghosts: Instruction Following as Bayesian State Tracking

Peter Anderson, Ayush Shrivastava, Devi Parikh, Dhruv Batra, Stefan Lee

Hierarchical Reinforcement Learning with Advantage-Based Auxiliary Rewards

Siyuan Li, Rui Wang, Minxue Tang, Chongjie Zhang

Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates

Carlos Riquelme, Hugo Penedones, Damien Vincent, Hartmut Maennel, Sylvain Gelly

On the Hardness of Robust Classification

Pascale Gourdeau, Varun Kanade, Marta Kwiatkowska, James Worrell

You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle

Dinghuai Zhang, Tianyuan Zhang, Yiping Lu, Zhanxing Zhu, Bin Dong

Coresets for Clustering with Fairness Constraints

Lingxiao Huang, Shaofeng Jiang, Nisheeth Vishnoi

Communication-Efficient Distributed Blockwise Momentum SGD with Error-Feedback

Shuai Zheng, Ziyue Huang, James Kwok

Optimal Analysis of Subset-Selection Based L_p Low-Rank Approximation

Chen Dan, Hong Wang, Hongyang Zhang, Yuchen Zhou, Pradeep K Ravikumar

The Implicit Metropolis-Hastings Algorithm

Kirill Neklyudov, Evgenii Egorov, Dmitry P Vetrov

Learnable Tree Filter for Structure-preserving Feature Transform

Lin Song, Yanwei Li, Zeming Li, Gang Yu, Hongbin Sun

Full-Gradient Representation for Neural Network Visualization

Suraj Srinivas, Francois Fleuret

Statistical Analysis of Nearest Neighbor Methods for Anomaly Detection

Xiaoyi Gu, Leman Akoglu, Alessandro Rinaldo

Unsupervised Keypoint Learning for Guiding Class-Conditional Video Prediction

Yunji Kim, Seonghyeon Nam, In Cho, Seon Joo Kim

Unified Sample-Optimal Property Estimation in Near-Linear Time

Yi Hao, Alon Orlitsky

Maximum Entropy Monte-Carlo Planning

Chenjun Xiao, Ruitong Huang, Jincheng Mei, Dale Schuurmans, Martin Müller

Fooling Neural Network Interpretations via Adversarial Model Manipulation

Juyeon Heo, Sunghwan Joo, Taesup Moon

Infra-slow brain dynamics as a marker for cognitive function and decline

Shagun Ajmera, Shreya Rajagopal, Razi Rehman, Devarajan Sridharan

List-decodable Linear Regression

Sushrut Karmalkar, Adam Klivans, Pravesh Kothari

Coresets for Archetypal Analysis

Sebastian Mair, Ulf Brefeld

Characterizing Bias in Classifiers using Generative Models

Daniel McDuff, Shuang Ma, Yale Song, Ashish Kapoor

Approximate Feature Collisions in Neural Nets

Ke Li, Tianhao Zhang, Jitendra Malik

Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks

Gaël Letarte, Pascal Germain, Benjamin Guedj, François Laviolette

Efficient Symmetric Norm Regression via Linear Sketching

Zhao Song, Ruosong Wang, Lin Yang, Hongyang Zhang, Peilin Zhong

Kernel-Based Approaches for Sequence Modeling: Connections to Neural Methods

Kevin Liang, Guoyin Wang, Yitong Li, Ricardo Henao, Lawrence Carin

Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse

James Lucas, George Tucker, Roger B Grosse, Mohammad Norouzi

Adversarial Fisher Vectors for Unsupervised Representation Learning

Shuangfei Zhai, Walter Talbott, Carlos Guestrin, Joshua Susskind

Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness

Andrey Malinin, Mark Gales

Backpropagation-Friendly Eigendecomposition

Wei Wang, Zheng Dang, Yinlin Hu, Pascal Fua, Mathieu Salzmann

Direct Estimation of Differential Functional Graphical Models

Boxin Zhao, Y. Samuel Wang, Mladen Kolar

From voxels to pixels and back: Self-supervision in natural-image reconstruction from fMRI

Roman Beliy, Guy Gaziv, Assaf Hoogi, Francesca Strappini, Tal Golan

Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond

Xuechen Li, Yi Wu, Lester Mackey, Murat A Erdogdu

Learning Hierarchical Priors in VAEs

Alexej Klushyn, Nutan Chen, Richard Kurle, Botond Cseke, Patrick van der Smagt

Dancing to Music

Hsin-Ying Lee, Xiaodong Yang, Ming-Yu Liu, Ting-Chun Wang, Yu-Ding Lu

Policy Evaluation with Latent Confounders via Optimal Balance

Andrew Bennett, Nathan Kallus

Sparse Variational Inference: Bayesian Coresets from Scratch

Trevor Campbell, Boyan Beronov

Partitioning Structure Learning for Segmented Linear Regression Trees

Xiangyu Zheng, Song Xi Chen

Embedding Symbolic Knowledge into Deep Networks

Yaqi Xie, Ziwei Xu, Mohan S Kankanhalli, Kuldeep S Meel, Harold Soh

Neural Spline Flows

Conor Durkan, Artur Bekasov, Iain Murray, George Papamakarios

Reconciling meta-learning and continual learning with online mixtures of tasks

Ghassen Jerfel, Erin Grant, Tom Griffiths, Katherine A. Heller

Minimax Optimal Estimation of Approximate Differential Privacy on Neighboring Databases

Xiyang Liu, Sewoong Oh

Accelerating Rescaled Gradient Descent: Fast Optimization of Smooth Functions

Ashia C Wilson, Lester Mackey, Andre Wibisono

Sobolev Independence Criterion

Youssef Mroueh, Tom Sercu, Mattia Rigotti, Inkit Padhi, Cicero Nogueira dos Santos

Structured Prediction with Projection Oracles

Mathieu Blondel

Hierarchical Decision Making by Generating and Following Natural Language Instructions

Hengyuan Hu, Denis Yarats, Qucheng Gong, Yuandong Tian, Mike Lewis

Faster width-dependent algorithm for mixed packing and covering LPs

Digvijay Boob, Saurabh Sawlani, Di Wang

Breaking the Glass Ceiling for Embedding-Based Classifiers for Large Output Spaces

Chuan Guo, Ali Mousavi, Xiang Wu, Daniel N Holtmann-Rice, Satyen Kale

Uncertainty on Asynchronous Time Event Prediction

Marin Biloš, Bertrand Charpentier, Stephan Günnemann

Transfer Anomaly Detection by Inferring Latent Domain Representations

Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara

Offline Contextual Bayesian Optimization

Ian Char, Youngseog Chung, Willie Neiswanger, Kirthevasan Kandasamy, Andrew Oakleigh Nelson

Logarithmic Regret for Online Control

Naman Agarwal, Elad Hazan, Karan Singh

Correlation clustering with local objectives

Sanchit Kalhan, Konstantin Makarychev, Timothy Zhou

Defending Neural Backdoors via Generative Distribution Modeling

Ximing Qiao, Yukun Yang, Hai Li

Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs

Jonas Kubilius, Martin Schrimpf, Kohitij Kar, Rishi Rajalingham, Ha Hong

Towards Automatic Concept-based Explanations

Amirata Ghorbani, James Wexler, James Y Zou, Been Kim

A Polynomial Time Algorithm for Log-Concave Maximum Likelihood via Locally Exponential Families

Brian Axelrod, Ilias Diakonikolas, Alistair Stewart, Anastasios Sidiropoulos, Gregory Valiant

Towards Understanding the Importance of Shortcut Connections in Residual Networks

Tianyi Liu, Minshuo Chen, Mo Zhou, Simon S Du, Enlu Zhou

Neural Networks with Cheap Differential Operators

Ricky T. Q. Chen, David K. Duvenaud

Shallow RNN: Accurate Time-series Classification on Resource Constrained Devices

Don Dennis, Durmus Alp Emre Acar, Vikram Mandikal, Vinu Sankar Sadasivan, Venkatesh Saligrama

Group Retention when Using Machine Learning in Sequential Decision Making: the Interplay between User Dynamics and Fairness

Xueru Zhang, Mohammadmahdi Khaliligarekani, Cem Tekin, mingyan liu

Exponential Family Estimation via Adversarial Dynamics Embedding

Bo Dai, Zhen Liu, Hanjun Dai, Niao He, Arthur Gretton

Multivariate Sparse Coding of Nonstationary Covariances with Gaussian Processes

Rui Li

Communication-efficient Distributed SGD with Sketching

Nikita Ivkin, Daniel Rothchild, Enayat Ullah, Vladimir braverman, Ion Stoica

Addressing Failure Prediction by Learning Model Confidence

Charles Corbière, Nicolas Thome, Avner Bar-Hen, Matthieu Cord, Patrick Pérez

Rethinking Deep Neural Network Ownership Verification: Embedding Passports to Defeat Ambiguity Attacks

Lixin Fan, Kam Woh Ng, Chee Seng Chan

Chundi Liu, Guangwei Yu, Maksims Volkovs, Cheng Chang, Himanshu Rai

Adversarial training for free!

Ali Shafahi, Mahyar Najibi, Mohammad Amin Ghiasi, Zheng Xu, John Dickerson

Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain)

Mariya Toneva, Leila Wehbe

Comparison Against Task Driven Artificial Neural Networks Reveals Functional Properties in Mouse Visual Cortex

Jianghong Shi, Eric Shea-Brown, Michael Buice

Efficiently escaping saddle points on manifolds

Christopher Criscitiello, Nicolas Boumal

Fast Structured Decoding for Sequence Models

Zhiqing Sun, Zhuohan Li, Haoqing Wang, Di He, Zi Lin

Fast Efficient Hyperparameter Tuning for Policy Gradient Methods

Supratik Paul, Vitaly Kurin, Shimon Whiteson

Unsupervised Co-Learning on G-Manifolds Across Irreducible Representations

Yifeng Fan, Tingran Gao, Zhizhen Jane Zhao

Near Neighbor: Who is the Fairest of Them All?

Sariel Har-Peled, Sepideh Mahabadi

DATA: Differentiable ArchiTecture Approximation

Jianlong Chang, xinbang zhang, Yiwen Guo, GAOFENG MENG, SHIMING XIANG

Wasserstein Weisfeiler-Lehman Graph Kernels

Matteo Togninalli, Elisabetta Ghisu, Felipe Llinares-López, Bastian Rieck, Karsten Borgwardt

Robust Principal Component Analysis with Adaptive Neighbors

Rui Zhang, Hanghang Tong

The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the XAUC Metric

Nathan Kallus, Angela Zhou

Single-Model Uncertainties for Deep Learning

Natasa Tagasovska, David Lopez-paz

Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology

Nima Dehmamy, Albert-Laszlo Barabasi, Rose Yu

Greedy Sampling for Approximate Clustering in the Presence of Outliers

Aditya Bhaskara, Sharvaree Vadgama, Hong Xu

On Relating Explanations and Adversarial Examples

Alexey Ignatiev, Nina Narodytska, Joao Marques-Silva

Oblivious Sampling Algorithms for Private Data Analysis

Sajin Sasy, Olga Ohrimenko

Distributional Policy Optimization: An Alternative Approach for Continuous Control

Chen Tessler, Guy Tennenholtz, Shie Mannor

SPoC: Search-based Pseudocode to Code

Sumith Kulal, Panupong Pasupat, Kartik Chandra, Mina Lee, Oded Padon

Individual Regret in Cooperative Nonstochastic Multi-Armed Bandits

Yogev Bar-On, Yishay Mansour

Information Competing Process for Learning Diversified Representations

Jie Hu, Rongrong Ji, ShengChuan Zhang, Xiaoshuai Sun, Qixiang Ye

On the equivalence between graph isomorphism testing and function approximation with GNNs

Zhengdao Chen, Soledad Villar, Lei Chen, Joan Bruna

Optimal Best Markovian Arm Identification with Fixed Confidence

Vrettos Moulos

Dynamic Incentive-Aware Learning: Robust Pricing in Contextual Auctions

Negin Golrezaei, Adel Javanmard, Vahab Mirrokni

Learning Generalizable Device Placement Algorithms for Distributed Machine Learning

ravichandra addanki, Shaileshh Bojja Venkatakrishnan, Shreyan Gupta, Hongzi Mao, Mohammad Alizadeh

Learning Temporal Pose Estimation from Sparsely-Labeled Videos

Gedas Bertasius, Christoph Feichtenhofer, Du Tran, Jianbo Shi, Lorenzo Torresani

MintNet: Building Invertible Neural Networks with Masked Convolutions

Yang Song, Chenlin Meng, Stefano Ermon

Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components Analysis

David Clark, Jesse Livezey, Kristofer Bouchard

A Debiased MDI Feature Importance Measure for Random Forests

Xiao Li, Yu Wang, Sumanta Basu, Karl Kumbier, Bin Yu

Kernel quadrature with DPPs

Ayoub Belhadji, Rémi Bardenet, Pierre Chainais

Learning from brains how to regularize machines

Zhe Li, Wieland Brendel, Edgar Walker, Erick Cobos, Taliah Muhammad

Private Learning Implies Online Learning: An Efficient Reduction

Alon Gonen, Elad Hazan, Shay Moran

Meta Learning with Relational Information for Short Sequences

Yujia Xie, Haoming Jiang, Feng Liu, Tuo Zhao, Hongyuan Zha

Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Gradient Estimators for Reinforcement Learning

Gregory Farquhar, Shimon Whiteson, Jakob Foerster