NeurIPS 2018

1,009 papers

Unsupervised Learning of View-invariant Action Representations

Junnan Li, Yongkang Wong, Qi Zhao, Mohan S Kankanhalli

Inferring Networks From Random Walk-Based Node Similarities

Jeremy Hoskins, Cameron Musco, Christopher Musco, Babis Tsourakakis

Efficient Formal Safety Analysis of Neural Networks

Shiqi Wang, Kexin Pei, Justin Whitehouse, Junfeng Yang, Suman Jana

The Price of Privacy for Low-rank Factorization

Jalaj Upadhyay

A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks

Jeffrey Chan, Valerio Perrone, Jeffrey Spence, Paul Jenkins, Sara Mathieson

On preserving non-discrimination when combining expert advice

Avrim Blum, Suriya Gunasekar, Thodoris Lykouris, Nati Srebro

Algorithms and Theory for Multiple-Source Adaptation

Judy Hoffman, Mehryar Mohri, Ningshan Zhang

Recurrent World Models Facilitate Policy Evolution

David Ha, Jürgen Schmidhuber

Lifelong Inverse Reinforcement Learning

Jorge Mendez, Shashank Shivkumar, Eric Eaton

A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem

Sampath Kannan, Jamie H Morgenstern, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu

Bayesian Control of Large MDPs with Unknown Dynamics in Data-Poor Environments

Mahdi Imani, Seyede Fatemeh Ghoreishi, Ulisses M. Braga-Neto

Wasserstein Variational Inference

Luca Ambrogioni, Umut Güçlü, Yağmur Güçlütürk, Max Hinne, Marcel A. J. van Gerven

Pipe-SGD: A Decentralized Pipelined SGD Framework for Distributed Deep Net Training

Youjie Li, Mingchao Yu, Songze Li, Salman Avestimehr, Nam Sung Kim

Scalable Robust Matrix Factorization with Nonconvex Loss

Quanming Yao, James Kwok

Leveraged volume sampling for linear regression

Michal Derezinski, Manfred K. Warmuth, Daniel J. Hsu

Automating Bayesian optimization with Bayesian optimization

Gustavo Malkomes, Roman Garnett

Disconnected Manifold Learning for Generative Adversarial Networks

Mahyar Khayatkhoei, Maneesh K. Singh, Ahmed Elgammal

Learning Deep Disentangled Embeddings With the F-Statistic Loss

Karl Ridgeway, Michael Mozer

Deep Dynamical Modeling and Control of Unsteady Fluid Flows

Jeremy Morton, Antony Jameson, Mykel J Kochenderfer, Freddie Witherden

Multitask Boosting for Survival Analysis with Competing Risks

Alexis Bellot, Mihaela van der Schaar

Plug-in Estimation in High-Dimensional Linear Inverse Problems: A Rigorous Analysis

Alyson K. Fletcher, Parthe Pandit, Sundeep Rangan, Subrata Sarkar, Philip Schniter

Understanding Regularized Spectral Clustering via Graph Conductance

Yilin Zhang, Karl Rohe

Supervised autoencoders: Improving generalization performance with unsupervised regularizers

Lei Le, Andrew Patterson, Martha White

DeepPINK: reproducible feature selection in deep neural networks

Yang Lu, Yingying Fan, Jinchi Lv, William Stafford Noble

Compact Representation of Uncertainty in Clustering

Craig Greenberg, Nicholas Monath, Ari Kobren, Patrick Flaherty, Andrew McGregor

Semidefinite relaxations for certifying robustness to adversarial examples

Aditi Raghunathan, Jacob Steinhardt, Percy Liang

Bayesian Alignments of Warped Multi-Output Gaussian Processes

Markus Kaiser, Clemens Otte, Thomas Runkler, Carl Henrik Ek

Differentially Private Uniformly Most Powerful Tests for Binomial Data

Jordan Awan, Aleksandra Slavković

Sanity Checks for Saliency Maps

Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt

Generalized Inverse Optimization through Online Learning

Chaosheng Dong, Yiran Chen, Bo Zeng

Multilingual Anchoring: Interactive Topic Modeling and Alignment Across Languages

Michelle Yuan, Benjamin Van Durme, Jordan L Ying

Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data

Ehsan Hajiramezanali, Siamak Zamani Dadaneh, Alireza Karbalayghareh, Mingyuan Zhou, Xiaoning Qian

PacGAN: The power of two samples in generative adversarial networks

Zinan Lin, Ashish Khetan, Giulia Fanti, Sewoong Oh

Forward Modeling for Partial Observation Strategy Games - A StarCraft Defogger

Gabriel Synnaeve, Zeming Lin, Jonas Gehring, Dan Gant, Vegard Mella

Attention in Convolutional LSTM for Gesture Recognition

Liang Zhang, Guangming Zhu, Lin Mei, Peiyi Shen, Syed Afaq Ali Shah

Uncertainty-Aware Attention for Reliable Interpretation and Prediction

Jay Heo, Hae Beom Lee, Saehoon Kim, Juho Lee, Kwang Joon Kim

Spectral Signatures in Backdoor Attacks

Brandon Tran, Jerry Li, Aleksander Madry

GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration

Jacob Gardner, Geoff Pleiss, Kilian Q. Weinberger, David Bindel, Andrew G Wilson

Meta-Gradient Reinforcement Learning

Zhongwen Xu, Hado P van Hasselt, David Silver

Are ResNets Provably Better than Linear Predictors?

Ohad Shamir

Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction

Roei Herzig, Moshiko Raboh, Gal Chechik, Jonathan Berant, Amir Globerson

Importance Weighting and Variational Inference

Justin Domke, Daniel R. Sheldon

Maximum Causal Tsallis Entropy Imitation Learning

Kyungjae Lee, Sungjoon Choi, Songhwai Oh

Adaptation to Easy Data in Prediction with Limited Advice

Tobias Sommer Thune, Yevgeny Seldin

Variational Learning on Aggregate Outputs with Gaussian Processes

Ho Chung Law, Dino Sejdinovic, Ewan Cameron, Tim Lucas, Seth Flaxman

Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning

Supasorn Suwajanakorn, Noah Snavely, Jonathan J Tompson, Mohammad Norouzi

Multi-Agent Generative Adversarial Imitation Learning

Jiaming Song, Hongyu Ren, Dorsa Sadigh, Stefano Ermon

Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization

Yizhe Zhang, Michel Galley, Jianfeng Gao, Zhe Gan, Xiujun Li

Inferring Latent Velocities from Weather Radar Data using Gaussian Processes

Rico Angell, Daniel R. Sheldon

Distilled Wasserstein Learning for Word Embedding and Topic Modeling

Hongteng Xu, Wenlin Wang, Wei Liu, Lawrence Carin

Learning convex polytopes with margin

Lee-Ad Gottlieb, Eran Kaufman, Aryeh Kontorovich, Gabriel Nivasch

Stanislav Morozov, Artem Babenko

Implicit Probabilistic Integrators for ODEs

Onur Teymur, Han Cheng Lie, Tim Sullivan, Ben Calderhead

Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks

Ali Shafahi, W. Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer

Factored Bandits

Julian Zimmert, Yevgeny Seldin

cpSGD: Communication-efficient and differentially-private distributed SGD

Naman Agarwal, Ananda Theertha Suresh, Felix Xinnan X Yu, Sanjiv Kumar, Brendan Mcmahan

First-order Stochastic Algorithms for Escaping From Saddle Points in Almost Linear Time

Yi Xu, Rong Jin, Tianbao Yang

Out-of-Distribution Detection using Multiple Semantic Label Representations

Gabi Shalev, Yossi Adi, Joseph Keshet

Diffusion Maps for Textual Network Embedding

Xinyuan Zhang, Yitong Li, Dinghan Shen, Lawrence Carin

Fighting Boredom in Recommender Systems with Linear Reinforcement Learning

Romain WARLOP, Alessandro Lazaric, Jeremie Mary

Mixture Matrix Completion

Daniel Pimentel-Alarcon

Learning to Reconstruct Shapes from Unseen Classes

Xiuming Zhang, Zhoutong Zhang, Chengkai Zhang, Josh Tenenbaum, Bill Freeman

Contextual Combinatorial Multi-armed Bandits with Volatile Arms and Submodular Reward

Lixing Chen, Jie Xu, Zhuo Lu

When do random forests fail?

Cheng Tang, Damien Garreau, Ulrike Von Luxburg

Simple, Distributed, and Accelerated Probabilistic Programming

Dustin Tran, Matthew W Hoffman, Dave Moore, Christopher Suter, Srinivas Vasudevan

Bayesian Semi-supervised Learning with Graph Gaussian Processes

Yin Cheng Ng, Nicolo Colombo, Ricardo Silva

Scalable Coordinated Exploration in Concurrent Reinforcement Learning

Maria Dimakopoulou, Ian Osband, Benjamin Van Roy

Unsupervised Learning of Object Landmarks through Conditional Image Generation

Tomas Jakab, Ankush Gupta, Hakan Bilen, Andrea Vedaldi

Why Is My Classifier Discriminatory?

Irene Chen, Fredrik D Johansson, David Sontag

Learning to Share and Hide Intentions using Information Regularization

DJ Strouse, Max Kleiman-Weiner, Josh Tenenbaum, Matt Botvinick, David J Schwab

Isolating Sources of Disentanglement in Variational Autoencoders

Ricky T. Q. Chen, Xuechen Li, Roger B Grosse, David K. Duvenaud

Unsupervised Cross-Modal Alignment of Speech and Text Embedding Spaces

Yu-An Chung, Wei-Hung Weng, Schrasing Tong, James Glass

Solving Non-smooth Constrained Programs with Lower Complexity than O(1/ε): A Primal-Dual Homotopy Smoothing Approach

Xiaohan Wei, Hao Yu, Qing Ling, Michael Neely

Structured Local Minima in Sparse Blind Deconvolution

Yuqian Zhang, Han-wen Kuo, John Wright

Dendritic cortical microcircuits approximate the backpropagation algorithm

João Sacramento, Rui Ponte Costa, Yoshua Bengio, Walter Senn

Information-theoretic Limits for Community Detection in Network Models

Chuyang Ke, Jean Honorio

Generalizing to Unseen Domains via Adversarial Data Augmentation

Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John C. Duchi, Vittorio Murino

Modelling and unsupervised learning of symmetric deformable object categories

James Thewlis, Hakan Bilen, Andrea Vedaldi

Variational Inference with Tail-adaptive f-Divergence

Dilin Wang, Hao Liu, Qiang Liu

Exponentiated Strongly Rayleigh Distributions

Zelda E. Mariet, Suvrit Sra, Stefanie Jegelka

Deep Predictive Coding Network with Local Recurrent Processing for Object Recognition

Kuan Han, Haiguang Wen, Yizhen Zhang, Di Fu, Eugenio Culurciello

Gaussian Process Prior Variational Autoencoders

Francesco Paolo Casale, Adrian Dalca, Luca Saglietti, Jennifer Listgarten, Nicolo Fusi

Phase Retrieval Under a Generative Prior

Paul Hand, Oscar Leong, Vlad Voroninski

Asymptotic optimality of adaptive importance sampling

François Portier, Bernard Delyon

BRUNO: A Deep Recurrent Model for Exchangeable Data

Iryna Korshunova, Jonas Degrave, Ferenc Huszar, Yarin Gal, Arthur Gretton

Heterogeneous Bitwidth Binarization in Convolutional Neural Networks

Joshua Fromm, Shwetak Patel, Matthai Philipose

Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation

Kush Bhatia, Aldo Pacchiano, Nicolas Flammarion, Peter L. Bartlett, Michael I. Jordan

Learning a Warping Distance from Unlabeled Time Series Using Sequence Autoencoders

Abubakar Abid, James Y Zou

Learning filter widths of spectral decompositions with wavelets

Haidar Khan, Bulent Yener

A Bridging Framework for Model Optimization and Deep Propagation

Risheng Liu, Shichao Cheng, xiaokun liu, Long Ma, Xin Fan

Uplift Modeling from Separate Labels

Ikko Yamane, Florian Yger, Jamal Atif, Masashi Sugiyama

Parameters as interacting particles: long time convergence and asymptotic error scaling of neural networks

Grant Rotskoff, Eric Vanden-Eijnden

The Spectrum of the Fisher Information Matrix of a Single-Hidden-Layer Neural Network

Jeffrey Pennington, Pratik Worah

Statistical and Computational Trade-Offs in Kernel K-Means

Daniele Calandriello, Lorenzo Rosasco

Gradient Descent for Spiking Neural Networks

Dongsung Huh, Terrence J. Sejnowski

Alternating optimization of decision trees, with application to learning sparse oblique trees

Miguel Á. Carreira-Perpiñán, Pooya Tavallali

Structure-Aware Convolutional Neural Networks

Jianlong Chang, Jie Gu, Lingfeng Wang, GAOFENG MENG, SHIMING XIANG

Amortized Inference Regularization

Rui Shu, Hung H Bui, Shengjia Zhao, Mykel J Kochenderfer, Stefano Ermon

PAC-Bayes Tree: weighted subtrees with guarantees

Tin D Nguyen, Samory Kpotufe

Neural Code Comprehension: A Learnable Representation of Code Semantics

Tal Ben-Nun, Alice Shoshana Jakobovits, Torsten Hoefler