NeurIPS 2019

1,428 papers

Region-specific Diffeomorphic Metric Mapping

Zhengyang Shen, Francois-Xavier Vialard, Marc Niethammer

Detecting Overfitting via Adversarial Examples

Roman Werpachowski, András György, Csaba Szepesvári

Differentiable Cloth Simulation for Inverse Problems

Junbang Liang, Ming Lin, Vladlen Koltun

Data-Dependence of Plateau Phenomenon in Learning with Neural Network --- Statistical Mechanical Analysis

Yuki Yoshida, Masato Okada

Energy-Inspired Models: Learning with Sampler-Induced Distributions

John Lawson, George Tucker, Bo Dai, Rajesh Ranganath

Random Quadratic Forms with Dependence: Applications to Restricted Isometry and Beyond

Arindam Banerjee, Qilong Gu, Vidyashankar Sivakumar, Steven Z. Wu

Better Transfer Learning with Inferred Successor Maps

Tamas Madarasz, Tim Behrens

Hamiltonian Neural Networks

Samuel Greydanus, Misko Dzamba, Jason Yosinski

Stochastic Variance Reduced Primal Dual Algorithms for Empirical Composition Optimization

Adithya M Devraj, Jianshu Chen

Neural Lyapunov Control

Ya-Chien Chang, Nima Roohi, Sicun Gao

Weighted Linear Bandits for Non-Stationary Environments

Yoan Russac, Claire Vernade, Olivier Cappe

Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies

Yonathan Efroni, Nadav Merlis, Mohammad Ghavamzadeh, Shie Mannor

Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates

Sharan Vaswani, Aaron Mishkin, Issam Laradji, Mark Schmidt, Gauthier Gidel

Modeling Tabular data using Conditional GAN

Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, Kalyan Veeramachaneni

On Learning Over-parameterized Neural Networks: A Functional Approximation Perspective

Lili Su, Pengkun Yang

The Option Keyboard: Combining Skills in Reinforcement Learning

Andre Barreto, Diana Borsa, Shaobo Hou, Gheorghe Comanici, Eser Aygün

Solving a Class of Non-Convex Min-Max Games Using Iterative First Order Methods

Maher Nouiehed, Maziar Sanjabi, Tianjian Huang, Jason Lee, Meisam Razaviyayn

Escaping from saddle points on Riemannian manifolds

Yue Sun, Nicolas Flammarion, Maryam Fazel

A Latent Variational Framework for Stochastic Optimization

Philippe Casgrain

A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks

Hadi Salman, Greg Yang, Huan Zhang, Cho-jui Hsieh, Pengchuan Zhang

Sample Complexity of Learning Mixture of Sparse Linear Regressions

Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor, Soumyabrata Pal

Inverting Deep Generative models, One layer at a time

Qi Lei, Ajil Jalal, Inderjit S Dhillon, Alexandros G Dimakis

Diffusion Improves Graph Learning

Johannes Gasteiger, Stefan Weißenberger, Stephan Günnemann

What the Vec? Towards Probabilistically Grounded Embeddings

Carl Allen, Ivana Balazevic, Timothy Hospedales

Nonlinear scaling of resource allocation in sensory bottlenecks

Laura Rose Edmondson, Alejandro Jimenez Rodriguez, Hannes P. Saal

DetNAS: Backbone Search for Object Detection

Yukang Chen, Tong Yang, Xiangyu Zhang, GAOFENG MENG, Xinyu Xiao

Turbo Autoencoder: Deep learning based channel codes for point-to-point communication channels

Yihan Jiang, Hyeji Kim, Himanshu Asnani, Sreeram Kannan, Sewoong Oh

Learning Neural Networks with Adaptive Regularization

Han Zhao, Yao-Hung Hubert Tsai, Ruslan Salakhutdinov, Geoffrey J. Gordon

ANODEV2: A Coupled Neural ODE Framework

Tianjun Zhang, Zhewei Yao, Amir Gholami, Joseph E Gonzalez, Kurt Keutzer

Neural Trust Region/Proximal Policy Optimization Attains Globally Optimal Policy

Boyi Liu, Qi Cai, Zhuoran Yang, Zhaoran Wang

Structured and Deep Similarity Matching via Structured and Deep Hebbian Networks

Dina Obeid, Hugo Ramambason, Cengiz Pehlevan

Multi-Criteria Dimensionality Reduction with Applications to Fairness

Uthaipon Tantipongpipat, Samira Samadi, Mohit Singh, Jamie H Morgenstern, Santosh Vempala

Regularizing Trajectory Optimization with Denoising Autoencoders

Rinu Boney, Norman Di Palo, Mathias Berglund, Alexander Ilin, Juho Kannala

Efficient Smooth Non-Convex Stochastic Compositional Optimization via Stochastic Recursive Gradient Descent

Wenqing Hu, Chris Junchi Li, Xiangru Lian, Ji Liu, Huizhuo Yuan

Augmented Neural ODEs

Emilien Dupont, Arnaud Doucet, Yee Whye Teh

Locality-Sensitive Hashing for f-Divergences: Mutual Information Loss and Beyond

Lin Chen, Hossein Esfandiari, Gang Fu, Vahab Mirrokni

Causal Regularization

Dominik Janzing

Generalization in multitask deep neural classifiers: a statistical physics approach

Anthony Ndirango, Tyler Lee

High-Quality Self-Supervised Deep Image Denoising

Samuli Laine, Tero Karras, Jaakko Lehtinen, Timo Aila

Batched Multi-armed Bandits Problem

Zijun Gao, Yanjun Han, Zhimei Ren, Zhengqing Zhou

MaCow: Masked Convolutional Generative Flow

Xuezhe Ma, Xiang Kong, Shanghang Zhang, Eduard Hovy

Likelihood-Free Overcomplete ICA and Applications In Causal Discovery

Chenwei DING, Mingming Gong, Kun Zhang, Dacheng Tao

Assessing Social and Intersectional Biases in Contextualized Word Representations

Yi Chern Tan, L. Elisa Celis

Multiclass Performance Metric Elicitation

Gaurush Hiranandani, Shant Boodaghians, Ruta Mehta, Oluwasanmi O Koyejo

TAB-VCR: Tags and Attributes based VCR Baselines

Jingxiang Lin, Unnat Jain, Alexander Schwing

Chirality Nets for Human Pose Regression

Raymond Yeh, Yuan-Ting Hu, Alexander Schwing

Gradient Dynamics of Shallow Univariate ReLU Networks

Francis Williams, Matthew Trager, Daniele Panozzo, Claudio Silva, Denis Zorin

Asymptotics for Sketching in Least Squares Regression

Edgar Dobriban, Sifan Liu

HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs

Naganand Yadati, Madhav Nimishakavi, Prateek Yadav, Vikram Nitin, Anand Louis

Root Mean Square Layer Normalization

Biao Zhang, Rico Sennrich

Likelihood Ratios for Out-of-Distribution Detection

Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin

Neural networks grown and self-organized by noise

Guruprasad Raghavan, Matt Thomson

Cascaded Dilated Dense Network with Two-step Data Consistency for MRI Reconstruction

Hao Zheng, Faming Fang, Guixu Zhang

Graph Normalizing Flows

Jenny Liu, Aviral Kumar, Jimmy Ba, Jamie Kiros, Kevin Swersky

Deep Multi-State Dynamic Recurrent Neural Networks Operating on Wavelet Based Neural Features for Robust Brain Machine Interfaces

Benyamin Allahgholizadeh Haghi, Spencer Kellis, Sahil Shah, Maitreyi Ashok, Luke Bashford

Reducing the variance in online optimization by transporting past gradients

Sébastien Arnold, Pierre-antoine Manzagol, Reza Babanezhad Harikandeh, Ioannis Mitliagkas, Nicolas Le Roux

Fast Convergence of Natural Gradient Descent for Over-Parameterized Neural Networks

Guodong Zhang, James Martens, Roger B Grosse

Exploring Algorithmic Fairness in Robust Graph Covering Problems

Aida Rahmattalabi, Phebe Vayanos, Anthony Fulginiti, Eric Rice, Bryan Wilder

Controllable Text-to-Image Generation

Bowen Li, Xiaojuan Qi, Thomas Lukasiewicz, Philip Torr

ADDIS: an adaptive discarding algorithm for online FDR control with conservative nulls

Jinjin Tian, Aaditya Ramdas

On two ways to use determinantal point processes for Monte Carlo integration

Guillaume Gautier, Rémi Bardenet, Michal Valko

Attentive State-Space Modeling of Disease Progression

Ahmed M. Alaa, Mihaela van der Schaar

Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness

Fanny Yang, Zuowen Wang, Christina Heinze-Deml

Learning to Confuse: Generating Training Time Adversarial Data with Auto-Encoder

Ji Feng, Qi-Zhi Cai, Zhi-Hua Zhou

Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks

Qiyang Li, Saminul Haque, Cem Anil, James Lucas, Roger B Grosse

MixMatch: A Holistic Approach to Semi-Supervised Learning

David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver

Globally Optimal Learning for Structured Elliptical Losses

Yoav Wald, Nofar Noy, Gal Elidan, Ami Wiesel

Accurate Uncertainty Estimation and Decomposition in Ensemble Learning

Jeremiah Liu, John Paisley, Marianthi-Anna Kioumourtzoglou, Brent Coull

Joint Optimization of Tree-based Index and Deep Model for Recommender Systems

Han Zhu, Daqing Chang, Ziru Xu, Pengye Zhang, Xiang Li

Calibration tests in multi-class classification: A unifying framework

David Widmann, Fredrik Lindsten, Dave Zachariah

Push-pull Feedback Implements Hierarchical Information Retrieval Efficiently

Xiao Liu, Xiaolong Zou, Zilong Ji, Gengshuo Tian, Yuanyuan Mi

Correlation Priors for Reinforcement Learning

Bastian Alt, Adrian Šošić, Heinz Koeppl

MonoForest framework for tree ensemble analysis

Igor Kuralenok, Vasilii Ershov, Igor Labutin

Ouroboros: On Accelerating Training of Transformer-Based Language Models

Qian Yang, Zhouyuan Huo, Wenlin Wang, Lawrence Carin

When to use parametric models in reinforcement learning?

Hado P van Hasselt, Matteo Hessel, John Aslanides

On the Fairness of Disentangled Representations

Francesco Locatello, Gabriele Abbati, Thomas Rainforth, Stefan Bauer, Bernhard Schölkopf

Trivializations for Gradient-Based Optimization on Manifolds

Mario Lezcano Casado

Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning

David Janz, Jiri Hron, Przemysław Mazur, Katja Hofmann, José Miguel Hernández-Lobato

Generalization Error Analysis of Quantized Compressive Learning

Xiaoyun Li, Ping Li

Direct Optimization through $\arg \max$ for Discrete Variational Auto-Encoder

Guy Lorberbom, Andreea Gane, Tommi Jaakkola, Tamir Hazan

Zero-shot Learning via Simultaneous Generating and Learning

Hyeonwoo Yu, Beomhee Lee

Hindsight Credit Assignment

Anna Harutyunyan, Will Dabney, Thomas Mesnard, Mohammad Gheshlaghi azar, Bilal Piot

Large Scale Adversarial Representation Learning

Jeff Donahue, Karen Simonyan

Learning Bayesian Networks with Low Rank Conditional Probability Tables

Adarsh Barik, Jean Honorio

Metalearned Neural Memory

Tsendsuren Munkhdalai, Alessandro Sordoni, Tong Wang, Adam Trischler

Kernel Instrumental Variable Regression

Rahul Singh, Maneesh Sahani, Arthur Gretton

Distributionally Robust Optimization and Generalization in Kernel Methods

Matthew Staib, Stefanie Jegelka

Semantic-Guided Multi-Attention Localization for Zero-Shot Learning

Yizhe Zhu, Jianwen Xie, Zhiqiang Tang, Xi Peng, Ahmed Elgammal

Robust Attribution Regularization

Jiefeng Chen, Xi Wu, Vaibhav Rastogi, Yingyu Liang, Somesh Jha

Compositional De-Attention Networks

Yi Tay, Anh Tuan Luu, Aston Zhang, Shuohang Wang, Siu Cheung Hui

Theoretical Analysis of Adversarial Learning: A Minimax Approach

Zhuozhuo Tu, Jingwei Zhang, Dacheng Tao

A First-Order Algorithmic Framework for Distributionally Robust Logistic Regression

JIAJIN LI, SEN HUANG, Anthony Man-Cho So

Convergence Guarantees for Adaptive Bayesian Quadrature Methods

Motonobu Kanagawa, Philipp Hennig

Learning-Based Low-Rank Approximations

Piotr Indyk, Ali Vakilian, Yang Yuan

RUDDER: Return Decomposition for Delayed Rewards

Jose A. Arjona-Medina, Michael Gillhofer, Michael Widrich, Thomas Unterthiner, Johannes Brandstetter

q-means: A quantum algorithm for unsupervised machine learning

Iordanis Kerenidis, Jonas Landman, Alessandro Luongo, Anupam Prakash

Implicit Semantic Data Augmentation for Deep Networks

Yulin Wang, Xuran Pan, Shiji Song, Hong Zhang, Gao Huang

Copulas as High-Dimensional Generative Models: Vine Copula Autoencoders

Natasa Tagasovska, Damien Ackerer, Thibault Vatter

Deep Generalized Method of Moments for Instrumental Variable Analysis

Andrew Bennett, Nathan Kallus, Tobias Schnabel

Learning to Predict Without Looking Ahead: World Models Without Forward Prediction

Daniel Freeman, David Ha, Luke Metz