NeurIPS 2018

1,009 papers

Moonshine: Distilling with Cheap Convolutions

Elliot J. Crowley, Gavin Gray, Amos J. Storkey

Weakly Supervised Dense Event Captioning in Videos

Xuguang Duan, Wenbing Huang, Chuang Gan, Jingdong Wang, Wenwu Zhu

Nonparametric Density Estimation under Adversarial Losses

Shashank Singh, Ananya Uppal, Boyue Li, Chun-Liang Li, Manzil Zaheer

Maximizing acquisition functions for Bayesian optimization

James Wilson, Frank Hutter, Marc Deisenroth

Learning to Decompose and Disentangle Representations for Video Prediction

Jun-Ting Hsieh, Bingbin Liu, De-An Huang, Li F Fei-Fei, Juan Carlos Niebles

Teaching Inverse Reinforcement Learners via Features and Demonstrations

Luis Haug, Sebastian Tschiatschek, Adish Singla

Deep Functional Dictionaries: Learning Consistent Semantic Structures on 3D Models from Functions

Minhyuk Sung, Hao Su, Ronald Yu, Leonidas Guibas

Reducing Network Agnostophobia

Akshay Raj Dhamija, Manuel Günther, Terrance Boult

Toddler-Inspired Visual Object Learning

Sven Bambach, David Crandall, Linda Smith, Chen Yu

3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data

Maurice Weiler, Mario Geiger, Max Welling, Wouter Boomsma, Taco S Cohen

Robust Learning of Fixed-Structure Bayesian Networks

Yu Cheng, Ilias Diakonikolas, Daniel Kane, Alistair Stewart

Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks

Yusuke Tsuzuku, Issei Sato, Masashi Sugiyama

A convex program for bilinear inversion of sparse vectors

Alireza Aghasi, Ali Ahmed, Paul Hand, Babhru Joshi

Fast Approximate Natural Gradient Descent in a Kronecker Factored Eigenbasis

Thomas George, César Laurent, Xavier Bouthillier, Nicolas Ballas, Pascal Vincent

Bandit Learning in Concave N-Person Games

Mario Bravo, David Leslie, Panayotis Mertikopoulos

A Probabilistic U-Net for Segmentation of Ambiguous Images

Simon Kohl, Bernardino Romera-Paredes, Clemens Meyer, Jeffrey De Fauw, Joseph R. Ledsam

Designing by Training: Acceleration Neural Network for Fast High-Dimensional Convolution

Longquan Dai, Liang Tang, Yuan Xie, Jinhui Tang

But How Does It Work in Theory? Linear SVM with Random Features

Yitong Sun, Anna Gilbert, Ambuj Tewari

Low-Rank Tucker Decomposition of Large Tensors Using TensorSketch

Osman Asif Malik, Stephen Becker

Neural Voice Cloning with a Few Samples

Sercan Arik, Jitong Chen, Kainan Peng, Wei Ping, Yanqi Zhou

Communication Compression for Decentralized Training

Hanlin Tang, Shaoduo Gan, Ce Zhang, Tong Zhang, Ji Liu

Direct Runge-Kutta Discretization Achieves Acceleration

Jingzhao Zhang, Aryan Mokhtari, Suvrit Sra, Ali Jadbabaie

Learning to Exploit Stability for 3D Scene Parsing

Yilun Du, Zhijian Liu, Hector Basevi, Ales Leonardis, Bill Freeman

Multi-Task Learning as Multi-Objective Optimization

Ozan Sener, Vladlen Koltun

Collaborative Learning for Deep Neural Networks

Guocong Song, Wei Chai

Large Margin Deep Networks for Classification

Gamaleldin Elsayed, Dilip Krishnan, Hossein Mobahi, Kevin Regan, Samy Bengio

Fully Neural Network Based Speech Recognition on Mobile and Embedded Devices

Jinhwan Park, Yoonho Boo, Iksoo Choi, Sungho Shin, Wonyong Sung

Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior

Zi Wang, Beomjoon Kim, Leslie Pack Kaelbling

Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo

Marton Havasi, José Miguel Hernández-Lobato, Juan José Murillo-Fuentes

Invariant Representations without Adversarial Training

Daniel Moyer, Shuyang Gao, Rob Brekelmans, Aram Galstyan, Greg Ver Steeg

Processing of missing data by neural networks

Marek Śmieja, Łukasz Struski, Jacek Tabor, Bartosz Zieliński, Przemysław Spurek

Contextual Pricing for Lipschitz Buyers

Jieming Mao, Renato Leme, Jon Schneider

Global Non-convex Optimization with Discretized Diffusions

Murat A Erdogdu, Lester Mackey, Ohad Shamir

The Cluster Description Problem - Complexity Results, Formulations and Approximations

Ian Davidson, Antoine Gourru, S Ravi

Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance

Giulia Luise, Alessandro Rudi, Massimiliano Pontil, Carlo Ciliberto

Hybrid Macro/Micro Level Backpropagation for Training Deep Spiking Neural Networks

Yingyezhe Jin, Wenrui Zhang, Peng Li

Scaling the Poisson GLM to massive neural datasets through polynomial approximations

David Zoltowski, Jonathan W Pillow

Multiple-Step Greedy Policies in Approximate and Online Reinforcement Learning

Yonathan Efroni, Gal Dalal, Bruno Scherrer, Shie Mannor

An Off-policy Policy Gradient Theorem Using Emphatic Weightings

Ehsan Imani, Eric Graves, Martha White

Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization

Blake E Woodworth, Jialei Wang, Adam Smith, Brendan Mcmahan, Nati Srebro

Predictive Uncertainty Estimation via Prior Networks

Andrey Malinin, Mark Gales

Towards Robust Interpretability with Self-Explaining Neural Networks

Tommi Jaakkola, David Alvarez-Melis, David Alvarez Melis

Inexact trust-region algorithms on Riemannian manifolds

Hiroyuki Kasai, Bamdev Mishra

Approximation algorithms for stochastic clustering

David Harris, Shi Li, Aravind Srinivasan, Khoa Trinh, Thomas Pensyl

The challenge of realistic music generation: modelling raw audio at scale

Sander Dieleman, Aaron van den Oord, Karen Simonyan

Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models

Kurtland Chua, Roberto Calandra, Rowan McAllister, Sergey Levine

Non-Adversarial Mapping with VAEs

Yedid Hoshen

Early Stopping for Nonparametric Testing

Meimei Liu, Guang Cheng

Scalable Laplacian K-modes

Imtiaz Ziko, Eric Granger, Ismail Ben Ayed

Semi-crowdsourced Clustering with Deep Generative Models

Yucen Luo, TIAN TIAN, Jiaxin Shi, Jun Zhu, Bo Zhang

The Description Length of Deep Learning models

Léonard Blier, Yann Ollivier

Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences

Borja Balle, Gilles Barthe, Marco Gaboardi

Foreground Clustering for Joint Segmentation and Localization in Videos and Images

Abhishek Sharma

Mesh-TensorFlow: Deep Learning for Supercomputers

Noam Shazeer, Youlong Cheng, Niki Parmar, Dustin Tran, Ashish Vaswani

Near Optimal Exploration-Exploitation in Non-Communicating Markov Decision Processes

Ronan Fruit, Matteo Pirotta, Alessandro Lazaric

Confounding-Robust Policy Improvement

Nathan Kallus, Angela Zhou

Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions

Sara Magliacane, Thijs van Ommen, Tom Claassen, Stephan Bongers, Philip Versteeg

Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation

Edward Smith, Scott Fujimoto, David Meger

Unsupervised Text Style Transfer using Language Models as Discriminators

Zichao Yang, Zhiting Hu, Chris Dyer, Taylor Berg-Kirkpatrick, Eric P. Xing

Quantifying Learning Guarantees for Convex but Inconsistent Surrogates

Kirill Struminsky, Simon Lacoste-Julien, Anton Osokin

Automatic Program Synthesis of Long Programs with a Learned Garbage Collector

Amit Zohar, Lior Wolf

Rest-Katyusha: Exploiting the Solution's Structure via Scheduled Restart Schemes

Junqi Tang, Mohammad Golbabaee, Francis Bach, Mike E davies

Multi-Layered Gradient Boosting Decision Trees

Ji Feng, Yang Yu, Zhi-Hua Zhou

DropMax: Adaptive Variational Softmax

Hae Beom Lee, Juho Lee, Saehoon Kim, Eunho Yang, Sung Ju Hwang

PAC-Bayes bounds for stable algorithms with instance-dependent priors

Omar Rivasplata, Emilio Parrado-hernández, Shiliang Sun, Csaba Szepesvári, John S. Shawe-taylor

Coordinate Descent with Bandit Sampling

Farnood Salehi, Patrick Thiran, Elisa Celis

Zeroth-order (Non)-Convex Stochastic Optimization via Conditional Gradient and Gradient Updates

Krishnakumar Balasubramanian, Saeed Ghadimi

Learning from discriminative feature feedback

Sanjoy Dasgupta, Akansha Dey, Nicholas Roberts, Sivan Sabato

Navigating with Graph Representations for Fast and Scalable Decoding of Neural Language Models

Minjia Zhang, Wenhan Wang, Xiaodong Liu, Jianfeng Gao, Yuxiong He

Understanding Batch Normalization

Nils Bjorck, Carla P. Gomes, Bart Selman, Kilian Q. Weinberger

Scaling provable adversarial defenses

Eric Wong, Frank Schmidt, Jan Hendrik Metzen, J. Zico Kolter

Improved Algorithms for Collaborative PAC Learning

Huy Nguyen, Lydia Zakynthinou

Playing hard exploration games by watching YouTube

Yusuf Aytar, Tobias Pfaff, David Budden, Thomas Paine, Ziyu Wang

Graphical model inference: Sequential Monte Carlo meets deterministic approximations

Fredrik Lindsten, Jouni Helske, Matti Vihola

Constrained Cross-Entropy Method for Safe Reinforcement Learning

Min Wen, Ufuk Topcu

Backpropagation with Callbacks: Foundations for Efficient and Expressive Differentiable Programming

Fei Wang, James Decker, Xilun Wu, Gregory Essertel, Tiark Rompf

Efficient Anomaly Detection via Matrix Sketching

Vatsal Sharan, Parikshit Gopalan, Udi Wieder

Analysis of Krylov Subspace Solutions of Regularized Non-Convex Quadratic Problems

Yair Carmon, John C. Duchi

Adversarial Risk and Robustness: General Definitions and Implications for the Uniform Distribution

Dimitrios Diochnos, Saeed Mahloujifar, Mohammad Mahmoody

Causal Inference and Mechanism Clustering of A Mixture of Additive Noise Models

Shoubo Hu, Zhitang Chen, Vahid Partovi Nia, Laiwan Chan, Yanhui Geng

Depth-Limited Solving for Imperfect-Information Games

Noam Brown, Tuomas Sandholm, Brandon Amos

ATOMO: Communication-efficient Learning via Atomic Sparsification

Hongyi Wang, Scott Sievert, Shengchao Liu, Zachary Charles, Dimitris Papailiopoulos

Training Deep Neural Networks with 8-bit Floating Point Numbers

Naigang Wang, Jungwook Choi, Daniel Brand, Chia-Yu Chen, Kailash Gopalakrishnan

The promises and pitfalls of Stochastic Gradient Langevin Dynamics

Nicolas Brosse, Alain Durmus, Eric Moulines

Gradient Sparsification for Communication-Efficient Distributed Optimization

Jianqiao Wangni, Jialei Wang, Ji Liu, Tong Zhang

Contamination Attacks and Mitigation in Multi-Party Machine Learning

Jamie Hayes, Olga Ohrimenko

Provable Gaussian Embedding with One Observation

Ming Yu, Zhuoran Yang, Tuo Zhao, Mladen Kolar, Zhaoran Wang

Multi-value Rule Sets for Interpretable Classification with Feature-Efficient Representations

Tong Wang

Differentially Private k-Means with Constant Multiplicative Error

Uri Stemmer, Haim Kaplan

Unorganized Malicious Attacks Detection

Ming Pang, Wei Gao, Min Tao, Zhi-Hua Zhou

Hamiltonian Variational Auto-Encoder

Anthony L Caterini, Arnaud Doucet, Dino Sejdinovic

Chain of Reasoning for Visual Question Answering

Chenfei Wu, Jinlai Liu, Xiaojie Wang, Xuan Dong

Deepcode: Feedback Codes via Deep Learning

Hyeji Kim, Yihan Jiang, Sreeram Kannan, Sewoong Oh, Pramod Viswanath

The Convergence of Sparsified Gradient Methods

Dan Alistarh, Torsten Hoefler, Mikael Johansson, Nikola Konstantinov, Sarit Khirirat

Modular Networks: Learning to Decompose Neural Computation

Louis Kirsch, Julius Kunze, David Barber

Q-learning with Nearest Neighbors

Devavrat Shah, Qiaomin Xie

Diverse Ensemble Evolution: Curriculum Data-Model Marriage

Tianyi Zhou, Shengjie Wang, Jeff A. Bilmes

Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections

Xin Zhang, Armando Solar-Lezama, Rishabh Singh

Distributed k-Clustering for Data with Heavy Noise

Shi Li, Xiangyu Guo

Extracting Relationships by Multi-Domain Matching

Yitong Li, michael Murias, geraldine Dawson, David E Carlson