ICML 2018

621 papers

Improving Regression Performance with Distributional Losses

Ehsan Imani, Martha White

Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model

Hideaki Imamura, Issei Sato, Masashi Sugiyama

Black-box Adversarial Attacks with Limited Queries and Information

Andrew Ilyas, Logan Engstrom, Anish Athalye, Jessy Lin

Attention-based Deep Multiple Instance Learning

Maximilian Ilse, Jakub Tomczak, Max Welling

Deep Variational Reinforcement Learning for POMDPs

Maximilian Igl, Luisa Zintgraf, Tuan Anh Le, Frank Wood, Shimon Whiteson

Using Reward Machines for High-Level Task Specification and Decomposition in Reinforcement Learning

Rodrigo Toro Icarte, Toryn Klassen, Richard Valenzano, Sheila McIlraith

Decoupled Parallel Backpropagation with Convergence Guarantee

Zhouyuan Huo, Bin Gu, Yang, Heng Huang

Topological mixture estimation

Steve Huntsman

Neural Autoregressive Flows

Chin-Wei Huang, David Krueger, Alexandre Lacoste, Aaron Courville

Learning Hidden Markov Models from Pairwise Co-occurrences with Application to Topic Modeling

Kejun Huang, Xiao Fu, Nicholas Sidiropoulos

Learning Deep ResNet Blocks Sequentially using Boosting Theory

Furong Huang, Jordan Ash, John Langford, Robert Schapire

Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices

zengfeng Huang

Dissipativity Theory for Accelerating Stochastic Variance Reduction: A Unified Analysis of SVRG and Katyusha Using Semidefinite Programs

Bin Hu, Stephen Wright, Laurent Lessard

Does Distributionally Robust Supervised Learning Give Robust Classifiers?

Weihua Hu, Gang Niu, Issei Sato, Masashi Sugiyama

Variational Bayesian dropout: pitfalls and fixes

Jiri Hron, Alex Matthews, Zoubin Ghahramani

Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solution for Nonconvex Distributed Optimization Over Networks

Mingyi Hong, Meisam Razaviyayn, Jason Lee

Sound Abstraction and Decomposition of Probabilistic Programs

Steven Holtzen, Guy Broeck, Todd Millstein

CyCADA: Cycle-Consistent Adversarial Domain Adaptation

Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola

Fast Bellman Updates for Robust MDPs

Chin Pang Ho, Marek Petrik, Wolfram Wiesemann

Orthogonal Recurrent Neural Networks with Scaled Cayley Transform

Kyle Helfrich, Devin Willmott, Qiang Ye

Learning unknown ODE models with Gaussian processes

Markus Heinonen, Cagatay Yildiz, Henrik Mannerström, Jukka Intosalmi, Harri Lahdesmaki

Recurrent Predictive State Policy Networks

Ahmed Hefny, Zita Marinho, Wen Sun, Siddhartha Srinivasa, Geoffrey Gordon

Multicalibration: Calibration for the (Computationally-Identifiable) Masses

Ursula Hebert-Johnson, Michael Kim, Omer Reingold, Guy Rothblum

Fairness Without Demographics in Repeated Loss Minimization

Tatsunori Hashimoto, Megha Srivastava, Hongseok Namkoong, Percy Liang

Learning Memory Access Patterns

Milad Hashemi, Kevin Swersky, Jamie Smith, Grant Ayers, Heiner Litz

Deep Models of Interactions Across Sets

Jason Hartford, Devon Graham, Kevin Leyton-brown, Siamak Ravanbakhsh

Stein Variational Gradient Descent Without Gradient

JUN HAN, Qiang Liu

Candidates vs. Noises Estimation for Large Multi-Class Classification Problem

Lei Han, Yiheng Huang, Tong Zhang

K-Beam Minimax: Efficient Optimization for Deep Adversarial Learning

Jihun Hamm, Yung-kyun Noh

Comparison-Based Random Forests

Siavash Haghiri, Damien Garreau, Ulrike Luxburg

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, Sergey Levine

Latent Space Policies for Hierarchical Reinforcement Learning

Tuomas Haarnoja, Kristian Hartikainen, Pieter Abbeel, Sergey Levine

Shampoo: Preconditioned Stochastic Tensor Optimization

Vineet Gupta, Tomer Koren, Yoram Singer

Characterizing Implicit Bias in Terms of Optimization Geometry

Suriya Gunasekar, Jason Lee, Daniel Soudry, Nathan Srebro

Learning to search with MCTSnets

Arthur Guez, Theophane Weber, Ioannis Antonoglou, Karen Simonyan, Oriol Vinyals

Faster Derivative-Free Stochastic Algorithm for Shared Memory Machines

Bin Gu, Zhouyuan Huo, Cheng Deng, Heng Huang

Learning Policy Representations in Multiagent Systems

Aditya Grover, Maruan Al-Shedivat, Jayesh Gupta, Yuri Burda, Harrison Edwards

Visualizing and Understanding Atari Agents

Samuel Greydanus, Anurag Koul, Jonathan Dodge, Alan Fern

Learning One Convolutional Layer with Overlapping Patches

Surbhi Goel, Adam Klivans, Raghu Meka

Non-linear motor control by local learning in spiking neural networks

Aditya Gilra, Wulfram Gerstner

Robust and Scalable Models of Microbiome Dynamics

Travis Gibson, Georg Gerber

Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial Time

Asish Ghoshal, Jean Honorio

Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors

Soumya Ghosh, Jiayu Yao, Finale Doshi-velez

Linear Spectral Estimators and an Application to Phase Retrieval

Ramina Ghods, Andrew Lan, Tom Goldstein, Christoph Studer

Budgeted Experiment Design for Causal Structure Learning

AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Elias Bareinboim

Temporal Poisson Square Root Graphical Models

Sinong Geng, Zhaobin Kuang, Peggy Peissig, David Page

Conditional Neural Processes

Marta Garnelo, Dan Rosenbaum, Christopher Maddison, Tiago Ramalho, David Saxton

Structured Output Learning with Abstention: Application to Accurate Opinion Prediction

Alexandre Garcia, Chloé Clavel, Slim Essid, Florence d’Alché-Buc

Parallel Bayesian Network Structure Learning

Tian Gao, Dennis Wei

Spotlight: Optimizing Device Placement for Training Deep Neural Networks

Yuanxiang Gao, Li Chen, Baochun Li

Synthesizing Programs for Images using Reinforced Adversarial Learning

Yaroslav Ganin, Tejas Kulkarni, Igor Babuschkin, S. M. Ali Eslami, Oriol Vinyals

Parameterized Algorithms for the Matrix Completion Problem

Robert Ganian, Iyad Kanj, Sebastian Ordyniak, Stefan Szeider

Hyperbolic Entailment Cones for Learning Hierarchical Embeddings

Octavian Ganea, Gary Becigneul, Thomas Hofmann

Inductive Two-Layer Modeling with Parametric Bregman Transfer

Vignesh Ganapathiraman, Zhan Shi, Xinhua Zhang, Yaoliang Yu

Local Private Hypothesis Testing: Chi-Square Tests

Marco Gaboardi, Ryan Rogers

The Generalization Error of Dictionary Learning with Moreau Envelopes

ALEXANDROS GEORGOGIANNIS

Born Again Neural Networks

Tommaso Furlanello, Zachary Lipton, Michael Tschannen, Laurent Itti, Anima Anandkumar

Clipped Action Policy Gradient

Yasuhiro Fujita, Shin-ichi Maeda

Addressing Function Approximation Error in Actor-Critic Methods

Scott Fujimoto, Herke Hoof, David Meger

Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning

Ronan Fruit, Matteo Pirotta, Alessandro Lazaric, Ronald Ortner

Bilevel Programming for Hyperparameter Optimization and Meta-Learning

Luca Franceschi, Paolo Frasconi, Saverio Salzo, Riccardo Grazzi, Massimiliano Pontil

ADMM and Accelerated ADMM as Continuous Dynamical Systems

Guilherme Franca, Daniel Robinson, René Vidal

Generative Temporal Models with Spatial Memory for Partially Observed Environments

Marco Fraccaro, Danilo Rezende, Yori Zwols, Alexander Pritzel, S. M. Ali Eslami

Practical Contextual Bandits with Regression Oracles

Dylan Foster, Alekh Agarwal, Miroslav Dudík, Haipeng Luo, Robert Schapire

DiCE: The Infinitely Differentiable Monte Carlo Estimator

Jakob Foerster, Gregory Farquhar, Maruan Al-Shedivat, Tim Rocktäschel, Eric Xing

Automatic Goal Generation for Reinforcement Learning Agents

Carlos Florensa, David Held, XINYANG GENG, Pieter Abbeel

Closed-form Marginal Likelihood in Gamma-Poisson Matrix Factorization

Louis Filstroff, Alberto Lumbreras, Cédric Févotte

Nonparametric variable importance using an augmented neural network with multi-task learning

Jean Feng, Brian Williamson, Noah Simon, Marco Carone

Fourier Policy Gradients

Matthew Fellows, Kamil Ciosek, Shimon Whiteson

CRVI: Convex Relaxation for Variational Inference

Ghazal Fazelnia, John Paisley

Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator

Maryam Fazel, Rong Ge, Sham Kakade, Mehran Mesbahi

Efficient and Consistent Adversarial Bipartite Matching

Rizal Fathony, Sima Behpour, Xinhua Zhang, Brian Ziebart

More Robust Doubly Robust Off-policy Evaluation

Mehrdad Farajtabar, Yinlam Chow, Mohammad Ghavamzadeh

BOHB: Robust and Efficient Hyperparameter Optimization at Scale

Stefan Falkner, Aaron Klein, Frank Hutter

The Limits of Maxing, Ranking, and Preference Learning

Moein Falahatgar, Ayush Jain, Alon Orlitsky, Venkatadheeraj Pichapati, Vaishakh Ravindrakumar

Scalable Gaussian Processes with Grid-Structured Eigenfunctions (GP-GRIEF)

Trefor Evans, Prasanth Nair

IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures

Lasse Espeholt, Hubert Soyer, Rémi Munos, Karen Simonyan, Vlad Mnih

Parallel and Streaming Algorithms for K-Core Decomposition

Hossein Esfandiari, Silvio Lattanzi, Vahab Mirrokni

Beyond the One-Step Greedy Approach in Reinforcement Learning

Yonathan Efroni, Gal Dalal, Bruno Scherrer, Shie Mannor

Entropy-SGD optimizes the prior of a PAC-Bayes bound: Generalization properties of Entropy-SGD and data-dependent priors

Gintare Karolina Dziugaite, Daniel Roy

Computational Optimal Transport: Complexity by Accelerated Gradient Descent Is Better Than by Sinkhorn’s Algorithm

Pavel Dvurechensky, Alexander Gasnikov, Alexey Kroshnin

A Distributed Second-Order Algorithm You Can Trust

Celestine Duenner, Aurelien Lucchi, Matilde Gargiani, An Bian, Thomas Hofmann

Investigating Human Priors for Playing Video Games

Rachit Dubey, Pulkit Agrawal, Deepak Pathak, Tom Griffiths, Alexei Efros

Gradient Descent Learns One-hidden-layer CNN: Don’t be Afraid of Spurious Local Minima

Simon Du, Jason Lee, Yuandong Tian, Aarti Singh, Barnabás Póczos

On the Power of Over-parametrization in Neural Networks with Quadratic Activation

Simon Du, Jason Lee

Weakly Consistent Optimal Pricing Algorithms in Repeated Posted-Price Auctions with Strategic Buyer

Alexey Drutsa

Essentially No Barriers in Neural Network Energy Landscape

Felix Draxler, Kambis Veschgini, Manfred Salmhofer, Fred Hamprecht

Low-Rank Riemannian Optimization on Positive Semidefinite Stochastic Matrices with Applications to Graph Clustering

Ahmed Douik, Babak Hassibi

Randomized Block Cubic Newton Method

Nikita Doikov, Peter Richtarik, University Edinburgh

Probabilistic Recurrent State-Space Models

Andreas Doerr, Christian Daniel, Martin Schiegg, Nguyen-Tuong Duy, Stefan Schaal

Coordinated Exploration in Concurrent Reinforcement Learning

Maria Dimakopoulou, Benjamin Van Roy

Discovering and Removing Exogenous State Variables and Rewards for Reinforcement Learning

Thomas Dietterich, George Trimponias, Zhitang Chen

Noisin: Unbiased Regularization for Recurrent Neural Networks

Adji Bousso Dieng, Rajesh Ranganath, Jaan Altosaar, David Blei

Leveraging Well-Conditioned Bases: Streaming and Distributed Summaries in Minkowski p-Norms

Charlie Dickens, Graham Cormode, David Woodruff

Learning to Act in Decentralized Partially Observable MDPs

Jilles Dibangoye, Olivier Buffet

Alternating Randomized Block Coordinate Descent

Jelena Diakonikolas, Lorenzo Orecchia

Modeling Sparse Deviations for Compressed Sensing using Generative Models

Manik Dhar, Aditya Grover, Stefano Ermon

Variational Network Inference: Strong and Stable with Concrete Support

Amir Dezfouli, Edwin Bonilla, Richard Nock

Accurate Inference for Adaptive Linear Models

Yash Deshpande, Lester Mackey, Vasilis Syrgkanis, Matt Taddy

Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning

Stefan Depeweg, Jose-Miguel Hernandez-Lobato, Finale Doshi-velez, Steffen Udluft