ICML 2018

621 papers

Stochastic Video Generation with a Learned Prior

Emily Denton, Rob Fergus

Minibatch Gibbs Sampling on Large Graphical Models

Chris De Sa, Vincent Chen, Wing Wong

Escaping Saddles with Stochastic Gradients

Hadi Daneshmand, Jonas Köhler, Aurelien Lucchi, Thomas Hofmann

Asynchronous Byzantine Machine Learning (the case of SGD)

Georgios Damaskinos, El-Mahdi El-Mhamdi, Rachid Guerraoui, Rhicheek Patra, Mahsa Taziki

Compressing Neural Networks using the Variational Information Bottleneck

Bin Dai, Chen Zhu, Baining Guo, David Wipf

SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation

Bo Dai, Albert Shaw, Lihong Li, Lin Xiao, Niao He

Adversarial Attack on Graph Structured Data

Hanjun Dai, Hui Li, TIAN TIAN, Xin Huang, Lin Wang

Learning Steady-States of Iterative Algorithms over Graphs

Hanjun Dai, Zornitsa Kozareva, Bo Dai, Alex Smola, Le Song

Implicit Quantile Networks for Distributional Reinforcement Learning

Will Dabney, Georg Ostrovski, David Silver, Rémi Munos

Mix & Match Agent Curricula for Reinforcement Learning

Wojciech Czarnecki, Siddhant Jayakumar, Max Jaderberg, Leonard Hasenclever, Yee Whye Teh

Inference Suboptimality in Variational Autoencoders

Chris Cremer, Xuechen Li, David Duvenaud

Constrained Interacting Submodular Groupings

Andrew Cotter, Mahdi Milani Fard, Seungil You, Maya Gupta, Jeff Bilmes

Online Learning with Abstention

Corinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, Scott Yang

Efficient Model-Based Deep Reinforcement Learning with Variational State Tabulation

Dane Corneil, Wulfram Gerstner, Johanni Brea

GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms

Cédric Colas, Olivier Sigaud, Pierre-yves Oudeyer

Online Linear Quadratic Control

Alon Cohen, Avinatan Hasidim, Tomer Koren, Nevena Lazic, Yishay Mansour

On Acceleration with Noise-Corrupted Gradients

Michael Cohen, Jelena Diakonikolas, Lorenzo Orecchia

Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings

John Co-Reyes, YuXuan Liu, Abhishek Gupta, Benjamin Eysenbach, Pieter Abbeel

Stochastic Wasserstein Barycenters

Sebastian Claici, Edward Chien, Justin Solomon

An Iterative, Sketching-based Framework for Ridge Regression

Agniva Chowdhury, Jiasen Yang, Petros Drineas

Path Consistency Learning in Tsallis Entropy Regularized MDPs

Yinlam Chow, Ofir Nachum, Mohammad Ghavamzadeh

Structured Evolution with Compact Architectures for Scalable Policy Optimization

Krzysztof Choromanski, Mark Rowland, Vikas Sindhwani, Richard Turner, Adrian Weller

Learning a Mixture of Two Multinomial Logits

Flavio Chierichetti, Ravi Kumar, Andrew Tomkins

Extreme Learning to Rank via Low Rank Assumption

Minhao Cheng, Ian Davidson, Cho-jui Hsieh

Stochastic Training of Graph Convolutional Networks with Variance Reduction

Jianfei Chen, Jun Zhu, Le Song

End-to-End Learning for the Deep Multivariate Probit Model

Di Chen, Yexiang Xue, Carla Gomes

Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization

Jinghui Chen, Pan Xu, Lingxiao Wang, Jian Ma, Quanquan Gu

SADAGRAD: Strongly Adaptive Stochastic Gradient Methods

Zaiyi Chen, Yi Xu, Enhong Chen, Tianbao Yang

DRACO: Byzantine-resilient Distributed Training via Redundant Gradients

Lingjiao Chen, Hongyi Wang, Zachary Charles, Dimitris Papailiopoulos

Variational Inference and Model Selection with Generalized Evidence Bounds

Liqun Chen, Chenyang Tao, Ruiyi Zhang, Ricardo Henao, Lawrence Carin Duke

Learning to Explain: An Information-Theoretic Perspective on Model Interpretation

Jianbo Chen, Le Song, Martin Wainwright, Michael Jordan

Dynamical Isometry and a Mean Field Theory of RNNs: Gating Enables Signal Propagation in Recurrent Neural Networks

Minmin Chen, Jeffrey Pennington, Samuel Schoenholz

PixelSNAIL: An Improved Autoregressive Generative Model

Xi Chen, Nikhil Mishra, Mostafa Rohaninejad, Pieter Abbeel

Learning K-way D-dimensional Discrete Codes for Compact Embedding Representations

Ting Chen, Martin Renqiang Min, Yizhou Sun

Stein Points

Wilson Ye Chen, Lester Mackey, Jackson Gorham, François-Xavier Briol, Chris Oates

Scalable Bilinear Pi Learning Using State and Action Features

Yichen Chen, Lihong Li, Mengdi Wang

Continuous-Time Flows for Efficient Inference and Density Estimation

Changyou Chen, Chunyuan Li, Liqun Chen, Wenlin Wang, Yunchen Pu

Projection-Free Online Optimization with Stochastic Gradient: From Convexity to Submodularity

Lin Chen, Christopher Harshaw, Hamed Hassani, Amin Karbasi

Weakly Submodular Maximization Beyond Cardinality Constraints: Does Randomization Help Greedy?

Lin Chen, Moran Feldman, Amin Karbasi

GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks

Zhao Chen, Vijay Badrinarayanan, Chen-Yu Lee, Andrew Rabinovich

Hierarchical Deep Generative Models for Multi-Rate Multivariate Time Series

Zhengping Che, Sanjay Purushotham, Guangyu Li, Bo Jiang, Yan Liu

Hierarchical Clustering with Structural Constraints

Vaggos Chatziafratis, Rad Niazadeh, Moses Charikar

On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo

Niladri Chatterji, Nicolas Flammarion, Yian Ma, Peter Bartlett, Michael Jordan

Learning and Memorization

Satrajit Chatterjee

Stability and Generalization of Learning Algorithms that Converge to Global Optima

Zachary Charles, Dimitris Papailiopoulos

Adversarial Time-to-Event Modeling

Paidamoyo Chapfuwa, Chenyang Tao, Chunyuan Li, Courtney Page, Benjamin Goldstein

Conditional Noise-Contrastive Estimation of Unnormalised Models

Ciwan Ceylan, Michael U. Gutmann

Fair and Diverse DPP-Based Data Summarization

Elisa Celis, Vijay Keswani, Damian Straszak, Amit Deshpande, Tarun Kathuria

Adversarial Learning with Local Coordinate Coding

Jiezhang Cao, Yong Guo, Qingyao Wu, Chunhua Shen, Junzhou Huang

Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent

Trevor Campbell, Tamara Broderick

Improved large-scale graph learning through ridge spectral sparsification

Daniele Calandriello, Alessandro Lazaric, Ioannis Koutis, Michal Valko

Path-Level Network Transformation for Efficient Architecture Search

Han Cai, Jiacheng Yang, Weinan Zhang, Song Han, Yong Yu

Quasi-Monte Carlo Variational Inference

Alexander Buchholz, Florian Wenzel, Stephan Mandt

Predict and Constrain: Modeling Cardinality in Deep Structured Prediction

Nataly Brukhim, Amir Globerson

Matrix Norms in Data Streams: Faster, Multi-Pass and Row-Order

Vladimir braverman, Stephen Chestnut, Robert Krauthgamer, Yi Li, David Woodruff

QuantTree: Histograms for Change Detection in Multivariate Data Streams

Giacomo Boracchi, Diego Carrera, Cristiano Cervellera, Danilo Macciò

Prediction Rule Reshaping

Matt Bonakdarpour, sabyasachi chatterjee, Rina Foygel Barber, John Lafferty

A Progressive Batching L-BFGS Method for Machine Learning

Raghu Bollapragada, Jorge Nocedal, Dheevatsa Mudigere, Hao-Jun Shi, Ping Tak Peter Tang

NetGAN: Generating Graphs via Random Walks

Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, Stephan Günnemann

Optimizing the Latent Space of Generative Networks

Piotr Bojanowski, Armand Joulin, David Lopez-Pas, arthur szlam

Adaptive Sampled Softmax with Kernel Based Sampling

Guy Blanc, Steffen Rendle

Autoregressive Convolutional Neural Networks for Asynchronous Time Series

Mikolaj Binkowski, Gautier Marti, Philippe Donnat

Distributed Clustering via LSH Based Data Partitioning

Aditya Bhaskara, Maheshakya Wijewardena

signSGD: Compressed Optimisation for Non-Convex Problems

Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, Animashree Anandkumar

Understanding and Simplifying One-Shot Architecture Search

Gabriel Bender, Pieter-jan Kindermans, Barret Zoph, Vijay Vasudevan, Quoc Le

To Understand Deep Learning We Need to Understand Kernel Learning

Mikhail Belkin, SIYUAN MA, Soumik Mandal

Mutual Information Neural Estimation

Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeshwar, Sherjil Ozair, Yoshua Bengio

Gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks

Peter Bartlett, Dave Helmbold, Philip Long

Measuring abstract reasoning in neural networks

David Barrett, Felix Hill, Adam Santoro, Ari Morcos, Timothy Lillicrap

Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement

Andre Barreto, Diana Borsa, John Quan, Tom Schaul, David Silver

Testing Sparsity over Known and Unknown Bases

Siddharth Barman, Arnab Bhattacharyya, Suprovat Ghoshal

Learning to Coordinate with Coordination Graphs in Repeated Single-Stage Multi-Agent Decision Problems

Eugenio Bargiacchi, Timothy Verstraeten, Diederik Roijers, Ann Nowe, Hado Hasselt

Geodesic Convolutional Shape Optimization

Pierre Baque, Edoardo Remelli, Francois Fleuret, Pascal Fua

Bayesian Optimization of Combinatorial Structures

Ricardo Baptista, Matthias Poloczek

Classification from Pairwise Similarity and Unlabeled Data

Han Bao, Gang Niu, Masashi Sugiyama

Using Inherent Structures to design Lean 2-layer RBMs

Abhishek Bansal, Abhinav Anand, Chiranjib Bhattacharyya

Improved Training of Generative Adversarial Networks Using Representative Features

Duhyeon Bang, Hyunjung Shim

Improving Optimization for Models With Continuous Symmetry Breaking

Robert Bamler, Stephan Mandt

Differentially Private Database Release via Kernel Mean Embeddings

Matej Balog, Ilya Tolstikhin, Bernhard Schölkopf

Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients

Lukas Balles, Philipp Hennig

Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising

Borja Balle, Yu-Xiang Wang

Approximation Guarantees for Adaptive Sampling

Eric Balkanski, Yaron Singer

A Spline Theory of Deep Learning

Randall Balestriero, baraniuk

Spline Filters For End-to-End Deep Learning

Randall Balestriero, Romain Cosentino, Herve Glotin, Richard Baraniuk

The Mechanics of n-Player Differentiable Games

David Balduzzi, Sébastien Racanière, James Martens, Jakob Foerster, Karl Tuyls

Learning to Branch

Maria-Florina Balcan, Travis Dick, Tuomas Sandholm, Ellen Vitercik

A Boo(n) for Evaluating Architecture Performance

Ondrej Bajgar, Rudolf Kadlec, Jan Kleindienst

SMAC: Simultaneous Mapping and Clustering Using Spectral Decompositions

Chandrajit Bajaj, Tingran Gao, Zihang He, Qixing Huang, Zhenxiao Liang

Comparing Dynamics: Deep Neural Networks versus Glassy Systems

Marco Baity-Jesi, Levent Sagun, Mario Geiger, Stefano Spigler, Gerard Ben Arous

Greed is Still Good: Maximizing Monotone Submodular+Supermodular (BP) Functions

Wenruo Bai, Jeff Bilmes

Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing

Davide Bacciu, Federico Errica, Alessio Micheli

Synthesizing Robust Adversarial Examples

Anish Athalye, Logan Engstrom, Andrew Ilyas, Kevin Kwok

Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples

Anish Athalye, Nicholas Carlini, David Wagner

Lipschitz Continuity in Model-based Reinforcement Learning

Kavosh Asadi, Dipendra Misra, Michael Littman

Stronger Generalization Bounds for Deep Nets via a Compression Approach

Sanjeev Arora, Rong Ge, Behnam Neyshabur, Yi Zhang

On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization

Sanjeev Arora, Nadav Cohen, Elad Hazan

Efficient Gradient-Free Variational Inference using Policy Search

Oleg Arenz, Gerhard Neumann, Mingjun Zhong

Subspace Embedding and Linear Regression with Orlicz Norm

Alexandr Andoni, Chengyu Lin, Ying Sheng, Peilin Zhong, Ruiqi Zhong

MAGAN: Aligning Biological Manifolds

Matthew Amodio, Smita Krishnaswamy

Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory

Ron Amit, Ron Meir