NeurIPS 2020

1,898 papers

Uncertainty Quantification for Inferring Hawkes Networks

Haoyun Wang, Liyan Xie, Alex Cuozzo, Simon Mak, Yao Xie

Synthetic Data Generators -- Sequential and Private

Olivier Bousquet, Roi Livni, Shay Moran

Nonconvex Sparse Graph Learning under Laplacian Constrained Graphical Model

Jiaxi Ying, Jose Vinicius de Miranda Cardoso, Daniel Palomar

Neuronal Gaussian Process Regression

Johannes Friedrich

Correlation Robust Influence Maximization

Louis Chen, Divya Padmanabhan, Chee Chin Lim, Karthik Natarajan

Estimation and Imputation in Probabilistic Principal Component Analysis with Missing Not At Random Data

Aude Sportisse, Claire Boyer, Julie Josse

FedSplit: an algorithmic framework for fast federated optimization

Reese Pathak, Martin J. Wainwright

Curriculum learning for multilevel budgeted combinatorial problems

Adel Nabli, Margarida Carvalho

Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and Variance Reduction

Gen Li, Yuting Wei, Yuejie Chi, Yuantao Gu, Yuxin Chen

Learning Restricted Boltzmann Machines with Sparse Latent Variables

Guy Bresler, Rares-Darius Buhai

UWSOD: Toward Fully-Supervised-Level Capacity Weakly Supervised Object Detection

Yunhang Shen, Rongrong Ji, Zhiwei Chen, Yongjian Wu, Feiyue Huang

GramGAN: Deep 3D Texture Synthesis From 2D Exemplars

Tiziano Portenier, Siavash Arjomand Bigdeli, Orcun Goksel

Semi-Supervised Partial Label Learning via Confidence-Rated Margin Maximization

Wei Wang, Min-ling Zhang

Regularized linear autoencoders recover the principal components, eventually

Xuchan Bao, James Lucas, Sushant Sachdeva, Roger B Grosse

CoADNet: Collaborative Aggregation-and-Distribution Networks for Co-Salient Object Detection

Qijian Zhang, Runmin Cong, Junhui Hou, Chongyi Li, Yao Zhao

Profile Entropy: A Fundamental Measure for the Learnability and Compressibility of Distributions

Yi Hao, Alon Orlitsky

Exchangeable Neural ODE for Set Modeling

Yang Li, Haidong Yi, Christopher Bender, Siyuan Shan, Junier B Oliva

Evaluating and Rewarding Teamwork Using Cooperative Game Abstractions

Tom Yan, Christian Kroer, Alexander Peysakhovich

Hierarchical Poset Decoding for Compositional Generalization in Language

Yinuo Guo, Zeqi Lin, Jian-Guang Lou, Dongmei Zhang

Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence

Bastian Rieck, Tristan Yates, Christian Bock, Karsten Borgwardt, Guy Wolf

Sampling from a k-DPP without looking at all items

Daniele Calandriello, Michal Derezinski, Michal Valko

Bandit Samplers for Training Graph Neural Networks

Ziqi Liu, Zhengwei Wu, Zhiqiang Zhang, Jun Zhou, Shuang Yang

On Uniform Convergence and Low-Norm Interpolation Learning

Lijia Zhou, Danica J. Sutherland, Nati Srebro

Barking up the right tree: an approach to search over molecule synthesis DAGs

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

Denoising Diffusion Probabilistic Models

Jonathan Ho, Ajay N. Jain, Pieter Abbeel

What Makes for Good Views for Contrastive Learning?

Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid

The Mean-Squared Error of Double Q-Learning

Wentao Weng, Harsh Gupta, Niao He, Lei Ying, R. Srikant

Towards Interpretable Natural Language Understanding with Explanations as Latent Variables

Wangchunshu Zhou, Jinyi Hu, Hanlin Zhang, Xiaodan Liang, Maosong Sun

Learning Strategic Network Emergence Games

Rakshit Trivedi, Hongyuan Zha

Online Structured Meta-learning

Huaxiu Yao, Yingbo Zhou, Mehrdad Mahdavi, Zhenhui (Jessie) Li, Richard Socher

BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images

Thu H Nguyen-Phuoc, Christian Richardt, Long Mai, Yongliang Yang, Niloy Mitra

Multipole Graph Neural Operator for Parametric Partial Differential Equations

Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Andrew Stuart

Dynamic Regret of Policy Optimization in Non-Stationary Environments

Yingjie Fei, Zhuoran Yang, Zhaoran Wang, Qiaomin Xie

Probabilistic Linear Solvers for Machine Learning

Jonathan Wenger, Philipp Hennig

On Correctness of Automatic Differentiation for Non-Differentiable Functions

Wonyeol Lee, Hangyeol Yu, Xavier Rival, Hongseok Yang

On Efficiency in Hierarchical Reinforcement Learning

Zheng Wen, Doina Precup, Morteza Ibrahimi, Andre Barreto, Benjamin Van Roy

Neural Controlled Differential Equations for Irregular Time Series

Patrick Kidger, James Morrill, James Foster, Terry Lyons

Debiasing Distributed Second Order Optimization with Surrogate Sketching and Scaled Regularization

Michal Derezinski, Burak Bartan, Mert Pilanci, Michael W. Mahoney

Autoregressive Score Matching

Chenlin Meng, Lantao Yu, Yang Song, Jiaming Song, Stefano Ermon

Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs

Nikolaos Karalias, Andreas Loukas

Factor Graph Grammars

David Chiang, Darcey Riley

Compositional Visual Generation with Energy Based Models

Yilun Du, Shuang Li, Igor Mordatch

Most ReLU Networks Suffer from ℓ^2 Adversarial Perturbations

Amit Daniely, Hadas Shacham

Large-Scale Adversarial Training for Vision-and-Language Representation Learning

Zhe Gan, Yen-Chun Chen, Linjie Li, Chen Zhu, Yu Cheng

Bootstrapping neural processes

Juho Lee, Yoonho Lee, Jungtaek Kim, Eunho Yang, Sung Ju Hwang

A Dictionary Approach to Domain-Invariant Learning in Deep Networks

Ze Wang, Xiuyuan Cheng, Guillermo Sapiro, Qiang Qiu

Towards Scalable Bayesian Learning of Causal DAGs

Jussi Viinikka, Antti Hyttinen, Johan Pensar, Mikko Koivisto

Neural Power Units

Niklas Heim, Tomas Pevny, Vasek Smidl

The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning

Harm Van Seijen, Hadi Nekoei, Evan Racah, Sarath Chandar

Off-Policy Evaluation via the Regularized Lagrangian

Mengjiao Yang, Ofir Nachum, Bo Dai, Lihong Li, Dale Schuurmans

On the Convergence of Smooth Regularized Approximate Value Iteration Schemes

Elena Smirnova, Elvis DOHMATOB

Neutralizing Self-Selection Bias in Sampling for Sortition

Bailey Flanigan, Paul Gölz, Anupam Gupta, Ariel D Procaccia

Hyperparameter Ensembles for Robustness and Uncertainty Quantification

Florian Wenzel, Jasper Snoek, Dustin Tran, Rodolphe Jenatton

A Simple and Efficient Smoothing Method for Faster Optimization and Local Exploration

Kevin Scaman, Ludovic DOS SANTOS, Merwan Barlier, Igor Colin

Introducing Routing Uncertainty in Capsule Networks

Fabio De Sousa Ribeiro, Georgios Leontidis, Stefanos Kollias

SMYRF - Efficient Attention using Asymmetric Clustering

Giannis Daras, Nikita Kitaev, Augustus Odena, Alexandros G Dimakis

(De)Randomized Smoothing for Certifiable Defense against Patch Attacks

Alexander Levine, Soheil Feizi

Federated Principal Component Analysis

Andreas Grammenos, Rodrigo Mendoza Smith, Jon Crowcroft, Cecilia Mascolo

Benchmarking Deep Learning Interpretability in Time Series Predictions

Aya Abdelsalam Ismail, Mohamed Gunady, Hector Corrada Bravo, Soheil Feizi

Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes

Mengdi Xu, Wenhao Ding, Jiacheng Zhu, ZUXIN LIU, Baiming Chen

Interpretable and Personalized Apprenticeship Scheduling: Learning Interpretable Scheduling Policies from Heterogeneous User Demonstrations

Rohan Paleja, Andrew Silva, Letian Chen, Matthew Gombolay

Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems

Aman Sinha, Matthew O'Kelly, Russ Tedrake, John C. Duchi

Detecting Interactions from Neural Networks via Topological Analysis

Zirui Liu, Qingquan Song, Kaixiong Zhou, Ting-Hsiang Wang, Ying Shan

Pruning neural networks without any data by iteratively conserving synaptic flow

Hidenori Tanaka, Daniel Kunin, Daniel L Yamins, Surya Ganguli

Crush Optimism with Pessimism: Structured Bandits Beyond Asymptotic Optimality

Kwang-Sung Jun, Chicheng Zhang

From Boltzmann Machines to Neural Networks and Back Again

Surbhi Goel, Adam Klivans, Frederic Koehler

The Power of Comparisons for Actively Learning Linear Classifiers

Max Hopkins, Daniel Kane, Shachar Lovett

Improving Natural Language Processing Tasks with Human Gaze-Guided Neural Attention

Ekta Sood, Simon Tannert, Philipp Mueller, Andreas Bulling

Generative 3D Part Assembly via Dynamic Graph Learning

jialei huang, Guanqi Zhan, Qingnan Fan, Kaichun Mo, Lin Shao

Proximity Operator of the Matrix Perspective Function and its Applications

Joong-Ho (Johann) Won

Multi-task Causal Learning with Gaussian Processes

Virginia Aglietti, Theodoros Damoulas, Mauricio Alvarez, Javier Gonzalez

Minibatch vs Local SGD for Heterogeneous Distributed Learning

Blake E Woodworth, Kumar Kshitij Patel, Nati Srebro

Subgroup-based Rank-1 Lattice Quasi-Monte Carlo

Yueming LYU, Yuan Yuan, Ivor W. Tsang

Unsupervised Data Augmentation for Consistency Training

Qizhe Xie, Zihang Dai, Eduard Hovy, Thang Luong, Quoc V Le

Learning Kernel Tests Without Data Splitting

Jonas Kübler, Wittawat Jitkrittum, Bernhard Schölkopf, Krikamol Muandet

Demixed shared component analysis of neural population data from multiple brain areas

Yu Takagi, Steven Kennerley, Jun-ichiro Hirayama, Laurence Hunt

Boundary thickness and robustness in learning models

Yaoqing Yang, Rajiv Khanna, Yaodong Yu, Amir Gholami, Kurt Keutzer

On 1/n neural representation and robustness

Josue Nassar, Piotr Sokol, SueYeon Chung, Kenneth D Harris, ll Memming Park

Multi-task Batch Reinforcement Learning with Metric Learning

Jiachen Li, Quan Vuong, Shuang Liu, Minghua Liu, Kamil Ciosek

Wavelet Flow: Fast Training of High Resolution Normalizing Flows

Jason J. Yu, Konstantinos G. Derpanis, Marcus A Brubaker

Neurosymbolic Reinforcement Learning with Formally Verified Exploration

Greg Anderson, Abhinav Verma, Isil Dillig, Swarat Chaudhuri

Optimal Adaptive Electrode Selection to Maximize Simultaneously Recorded Neuron Yield

John Choi, Krishan Kumar, Mohammad Khazali, Katie Wingel, Mahdi Choudhury

How does This Interaction Affect Me? Interpretable Attribution for Feature Interactions

Michael Tsang, Sirisha Rambhatla, Yan Liu

Predicting Training Time Without Training

Luca Zancato, Alessandro Achille, Avinash Ravichandran, Rahul Bhotika, Stefano Soatto

Reinforcement Learning with General Value Function Approximation: Provably Efficient Approach via Bounded Eluder Dimension

Ruosong Wang, Ruslan Salakhutdinov, Lin Yang

Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers

Kiwon Um, Robert Brand, Yun (Raymond) Fei, Philipp Holl, Nils Thuerey

Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence

Thomas Sutter, Imant Daunhawer, Julia Vogt

Continuous Object Representation Networks: Novel View Synthesis without Target View Supervision

Nicolai Hani, Selim Engin, Jun-Jee Chao, Volkan Isler

Sparse Symplectically Integrated Neural Networks

Daniel DiPietro, Shiying Xiong, Bo Zhu

Estimating decision tree learnability with polylogarithmic sample complexity

Guy Blanc, Neha Gupta, Jane Lange, Li-Yang Tan

Meta-learning from Tasks with Heterogeneous Attribute Spaces

Tomoharu Iwata, Atsutoshi Kumagai

Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking

Anqi Wu, Estefany Kelly Buchanan, Matthew Whiteway, Michael Schartner, Guido Meijer

Learning Physical Graph Representations from Visual Scenes

Daniel Bear, Chaofei Fan, Damian Mrowca, Yunzhu Li, Seth Alter

Is Plug-in Solver Sample-Efficient for Feature-based Reinforcement Learning?

Qiwen Cui, Lin Yang

Differentially-Private Federated Linear Bandits

Abhimanyu Dubey, Alex `Sandy' Pentland

Revisiting Parameter Sharing for Automatic Neural Channel Number Search

Jiaxing Wang, Haoli Bai, Jiaxiang Wu, Xupeng Shi, Junzhou Huang

NeuMiss networks: differentiable programming for supervised learning with missing values.

Marine Le Morvan, Julie Josse, Thomas Moreau, Erwan Scornet, Gael Varoquaux

Model Agnostic Multilevel Explanations

Karthikeyan Natesan Ramamurthy, Bhanukiran Vinzamuri, Yunfeng Zhang, Amit Dhurandhar

Random Reshuffling is Not Always Better

Christopher M De Sa

GPU-Accelerated Primal Learning for Extremely Fast Large-Scale Classification

John T Halloran, David M Rocke