NeurIPS 2019

1,428 papers

Dimensionality reduction: theoretical perspective on practical measures

Yair Bartal, Nova Fandina, Ofer Neiman

Causal Confusion in Imitation Learning

Pim de Haan, Dinesh Jayaraman, Sergey Levine

Maximum Mean Discrepancy Gradient Flow

Michael Arbel, Anna Korba, Adil SALIM, Arthur Gretton

Blow: a single-scale hyperconditioned flow for non-parallel raw-audio voice conversion

Joan Serrà, Santiago Pascual, Carlos Segura Perales

Bayesian Joint Estimation of Multiple Graphical Models

Lingrui Gan, Xinming Yang, Naveen Narisetty, Feng Liang

Smoothing Structured Decomposable Circuits

Andy Shih, Guy Van den Broeck, Paul Beame, Antoine Amarilli

Multi-Resolution Weak Supervision for Sequential Data

Paroma Varma, Frederic Sala, Shiori Sagawa, Jason Fries, Daniel Fu

SCAN: A Scalable Neural Networks Framework Towards Compact and Efficient Models

Linfeng Zhang, Zhanhong Tan, Jiebo Song, Jingwei Chen, Chenglong Bao

Are Anchor Points Really Indispensable in Label-Noise Learning?

Xiaobo Xia, Tongliang Liu, Nannan Wang, Bo Han, Chen Gong

Connections Between Mirror Descent, Thompson Sampling and the Information Ratio

Julian Zimmert, Tor Lattimore

One-Shot Object Detection with Co-Attention and Co-Excitation

Ting-I Hsieh, Yi-Chen Lo, Hwann-Tzong Chen, Tyng-luh Liu

Data Parameters: A New Family of Parameters for Learning a Differentiable Curriculum

Shreyas Saxena, Oncel Tuzel, Dennis DeCoste

On Robustness of Principal Component Regression

Anish Agarwal, Devavrat Shah, Dennis Shen, Dogyoon Song

Self-Supervised Generalisation with Meta Auxiliary Learning

Shikun Liu, Andrew Davison, Edward Johns

Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks

Difan Zou, Ziniu Hu, Yewen Wang, Song Jiang, Yizhou Sun

Finding Friend and Foe in Multi-Agent Games

Jack Serrino, Max Kleiman-Weiner, David C. Parkes, Josh Tenenbaum

Lookahead Optimizer: k steps forward, 1 step back

Michael Zhang, James Lucas, Jimmy Ba, Geoffrey E. Hinton

Structured Graph Learning Via Laplacian Spectral Constraints

Sandeep Kumar, Jiaxi Ying, Jose Vinicius de Miranda Cardoso, Daniel Palomar

Regularized Weighted Low Rank Approximation

Frank Ban, David Woodruff, Richard Zhang

Limiting Extrapolation in Linear Approximate Value Iteration

Andrea Zanette, Alessandro Lazaric, Mykel J Kochenderfer, Emma Brunskill

Markov Random Fields for Collaborative Filtering

Harald Steck

Computing Linear Restrictions of Neural Networks

Matthew Sotoudeh, Aditya V Thakur

Incremental Scene Synthesis

Benjamin Planche, Xuejian Rong, Ziyan Wu, Srikrishna Karanam, Harald Kosch

Neural Shuffle-Exchange Networks - Sequence Processing in O(n log n) Time

Karlis Freivalds, Emīls Ozoliņš, Agris Šostaks

Optimal Sampling and Clustering in the Stochastic Block Model

Se-Young Yun, Alexandre Proutiere

Solving graph compression via optimal transport

Vikas Garg, Tommi Jaakkola

Adapting Neural Networks for the Estimation of Treatment Effects

Claudia Shi, David Blei, Victor Veitch

Conformal Prediction Under Covariate Shift

Ryan J Tibshirani, Rina Foygel Barber, Emmanuel Candes, Aaditya Ramdas

Memory Efficient Adaptive Optimization

Rohan Anil, Vineet Gupta, Tomer Koren, Yoram Singer

Heterogeneous Graph Learning for Visual Commonsense Reasoning

Weijiang Yu, Jingwen Zhou, Weihao Yu, Xiaodan Liang, Nong Xiao

Exploiting Local and Global Structure for Point Cloud Semantic Segmentation with Contextual Point Representations

Xu Wang, Jingming He, Lin Ma

Selecting Optimal Decisions via Distributionally Robust Nearest-Neighbor Regression

Ruidi Chen, Ioannis Paschalidis

Estimating Entropy of Distributions in Constant Space

Jayadev Acharya, Sourbh Bhadane, Piotr Indyk, Ziteng Sun

NeurVPS: Neural Vanishing Point Scanning via Conic Convolution

Yichao Zhou, Haozhi Qi, Jingwei Huang, Yi Ma

Private Testing of Distributions via Sample Permutations

Maryam Aliakbarpour, Ilias Diakonikolas, Daniel Kane, Ronitt Rubinfeld

Saccader: Improving Accuracy of Hard Attention Models for Vision

Gamaleldin Elsayed, Simon Kornblith, Quoc V Le

Decentralized sketching of low rank matrices

Rakshith Sharma Srinivasa, Kiryung Lee, Marius Junge, Justin Romberg

Noise-tolerant fair classification

Alex Lamy, Ziyuan Zhong, Aditya K Menon, Nakul Verma

SySCD: A System-Aware Parallel Coordinate Descent Algorithm

Nikolas Ioannou, Celestine Mendler-Dünner, Thomas Parnell

Perceiving the arrow of time in autoregressive motion

Kristof Meding, Dominik Janzing, Bernhard Schölkopf, Felix A. Wichmann

Non-Asymptotic Pure Exploration by Solving Games

Rémy Degenne, Wouter M. Koolen, Pierre Menard

The Parameterized Complexity of Cascading Portfolio Scheduling

Eduard Eiben, Robert Ganian, Iyad Kanj, Stefan Szeider

Robust Bi-Tempered Logistic Loss Based on Bregman Divergences

Ehsan Amid, Manfred K. Warmuth, Rohan Anil, Tomer Koren

Sampling Networks and Aggregate Simulation for Online POMDP Planning

Hao(Jackson) Cui, Roni Khardon

Curvilinear Distance Metric Learning

Shuo Chen, Lei Luo, Jian Yang, Chen Gong, Jun Li

Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with Dirichlet calibration

Meelis Kull, Miquel Perello Nieto, Markus Kängsepp, Telmo Silva Filho, Hao Song

Triad Constraints for Learning Causal Structure of Latent Variables

Ruichu Cai, Feng Xie, Clark Glymour, Zhifeng Hao, Kun Zhang

Efficient Meta Learning via Minibatch Proximal Update

Pan Zhou, Xiaotong Yuan, Huan Xu, Shuicheng Yan, Jiashi Feng

End to end learning and optimization on graphs

Bryan Wilder, Eric Ewing, Bistra Dilkina, Milind Tambe

Sequential Experimental Design for Transductive Linear Bandits

Tanner Fiez, Lalit Jain, Kevin G. Jamieson, Lillian Ratliff

Strategizing against No-regret Learners

Yuan Deng, Jon Schneider, Balasubramanian Sivan

PerspectiveNet: A Scene-consistent Image Generator for New View Synthesis in Real Indoor Environments

David Novotny, Ben Graham, Jeremy Reizenstein

Hierarchical Optimal Transport for Document Representation

Mikhail Yurochkin, Sebastian Claici, Edward Chien, Farzaneh Mirzazadeh, Justin M Solomon

Discriminator optimal transport

Akinori Tanaka

Same-Cluster Querying for Overlapping Clusters

Wasim Huleihel, Arya Mazumdar, Muriel Medard, Soumyabrata Pal

Connective Cognition Network for Directional Visual Commonsense Reasoning

Aming Wu, Linchao Zhu, Yahong Han, Yi Yang

Learning from Bad Data via Generation

Tianyu Guo, Chang Xu, Boxin Shi, Chao Xu, Dacheng Tao

L_DMI: A Novel Information-theoretic Loss Function for Training Deep Nets Robust to Label Noise

Yilun Xu, Peng Cao, Yuqing Kong, Yizhou Wang

Third-Person Visual Imitation Learning via Decoupled Hierarchical Controller

Pratyusha Sharma, Deepak Pathak, Abhinav Gupta

Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning

Chao Qu, Shie Mannor, Huan Xu, Yuan Qi, Le Song

Blocking Bandits

Soumya Basu, Rajat Sen, Sujay Sanghavi, Sanjay Shakkottai

From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox Optimization

Krzysztof M Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Vikas Sindhwani

UniXGrad: A Universal, Adaptive Algorithm with Optimal Guarantees for Constrained Optimization

Ali Kavis, Kfir Y. Levy, Francis Bach, Volkan Cevher

Accurate, reliable and fast robustness evaluation

Wieland Brendel, Jonas Rauber, Matthias Kümmerer, Ivan Ustyuzhaninov, Matthias Bethge

Controllable Unsupervised Text Attribute Transfer via Editing Entangled Latent Representation

Ke Wang, Hang Hua, Xiaojun Wan

Time Matters in Regularizing Deep Networks: Weight Decay and Data Augmentation Affect Early Learning Dynamics, Matter Little Near Convergence

Aditya Sharad Golatkar, Alessandro Achille, Stefano Soatto

MetaInit: Initializing learning by learning to initialize

Yann N. Dauphin, Samuel Schoenholz

Learning Hawkes Processes from a handful of events

Farnood Salehi, William Trouleau, Matthias Grossglauser, Patrick Thiran

Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel

Colin Wei, Jason Lee, Qiang Liu, Tengyu Ma

Reinforcement Learning with Convex Constraints

Sobhan Miryoosefi, Kianté Brantley, Hal Daume III, Miro Dudik, Robert E. Schapire

Multiview Aggregation for Learning Category-Specific Shape Reconstruction

Srinath Sridhar, Davis Rempe, Julien Valentin, Bouaziz Sofien, Leonidas Guibas

Multiple Futures Prediction

Charlie Tang, Ruslan Salakhutdinov

A Robust Non-Clairvoyant Dynamic Mechanism for Contextual Auctions

Yuan Deng, Sebastien Lahaie, Vahab Mirrokni

An Inexact Augmented Lagrangian Framework for Nonconvex Optimization with Nonlinear Constraints

Mehmet Fatih Sahin, Armin eftekhari, Ahmet Alacaoglu, Fabian Latorre, Volkan Cevher

Continual Unsupervised Representation Learning

Dushyant Rao, Francesco Visin, Andrei Rusu, Razvan Pascanu, Yee Whye Teh

Generalization Bounds for Neural Networks via Approximate Description Length

Amit Daniely, Elad Granot

Optimal Decision Tree with Noisy Outcomes

Su Jia, viswanath nagarajan, Fatemeh Navidi, R Ravi

Non-Stationary Markov Decision Processes, a Worst-Case Approach using Model-Based Reinforcement Learning

Erwan Lecarpentier, Emmanuel Rachelson

Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift

Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, D. Sculley

Powerset Convolutional Neural Networks

Chris Wendler, Markus Püschel, Dan Alistarh

Decentralized Cooperative Stochastic Bandits

David Martínez-Rubio, Varun Kanade, Patrick Rebeschini

Modular Universal Reparameterization: Deep Multi-task Learning Across Diverse Domains

Elliot Meyerson, Risto Miikkulainen

Putting An End to End-to-End: Gradient-Isolated Learning of Representations

Sindy Löwe, Peter O'Connor, Bastiaan Veeling

On the Ineffectiveness of Variance Reduced Optimization for Deep Learning

Aaron Defazio, Léon Bottou

Bayesian Batch Active Learning as Sparse Subset Approximation

Robert Pinsler, Jonathan Gordon, Eric Nalisnick, José Miguel Hernández-Lobato

No-Press Diplomacy: Modeling Multi-Agent Gameplay

Philip Paquette, Yuchen Lu, SETON STEVEN BOCCO, Max Smith, Satya O.-G.

Surrogate Objectives for Batch Policy Optimization in One-step Decision Making

Minmin Chen, Ramki Gummadi, Chris Harris, Dale Schuurmans

Shadowing Properties of Optimization Algorithms

Antonio Orvieto, Aurelien Lucchi

Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift

Stephan Rabanser, Stephan Günnemann, Zachary Lipton

GENO -- GENeric Optimization for Classical Machine Learning

Soeren Laue, Matthias Mitterreiter, Joachim Giesen

On Single Source Robustness in Deep Fusion Models

Taewan Kim, Joydeep Ghosh

Learning Multiple Markov Chains via Adaptive Allocation

Mohammad Sadegh Talebi, Odalric-ambrym Maillard

Random Path Selection for Continual Learning

Jathushan Rajasegaran, Munawar Hayat, Salman H Khan, Fahad Shahbaz Khan, Ling Shao

Curriculum-guided Hindsight Experience Replay

Meng Fang, Tianyi Zhou, Yali Du, Lei Han, Zhengyou Zhang

Categorized Bandits

Matthieu Jedor, Vianney Perchet, Jonathan Louedec

Algorithmic Guarantees for Inverse Imaging with Untrained Network Priors

Gauri Jagatap, Chinmay Hegde

Max-value Entropy Search for Multi-Objective Bayesian Optimization

Syrine Belakaria, Aryan Deshwal, Janardhan Rao Doppa

Numerically Accurate Hyperbolic Embeddings Using Tiling-Based Models

Tao Yu, Christopher M De Sa

Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation

Justin Domke, Daniel R. Sheldon

Rethinking the CSC Model for Natural Images

Dror Simon, Michael Elad