ICML 2018

621 papers

Efficient Neural Architecture Search via Parameters Sharing

Hieu Pham, Melody Guan, Barret Zoph, Quoc Le, Jeff Dean

Adaptive Three Operator Splitting

Fabian Pedregosa, Gauthier Gidel

High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach

Tim Pearce, Alexandra Brintrup, Mohamed Zaki, Andy Neely

PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos

Paavo Parmas, Carl Edward Rasmussen, Jan Peters, Kenji Doya

Image Transformer

Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, LUKASZ KAISER, Noam Shazeer

Time Limits in Reinforcement Learning

Fabio Pardo, Arash Tavakoli, Vitaly Levdik, Petar Kormushev

Learning Independent Causal Mechanisms

Giambattista Parascandolo, Niki Kilbertus, Mateo Rojas-Carulla, Bernhard Schölkopf

Stochastic Variance-Reduced Policy Gradient

Matteo Papini, Damiano Binaghi, Giuseppe Canonaco, Matteo Pirotta, Marcello Restelli

Max-Mahalanobis Linear Discriminant Analysis Networks

Tianyu Pang, Chao Du, Jun Zhu

Theoretical Analysis of Image-to-Image Translation with Adversarial Learning

Xudong Pan, Mi Zhang, Daizong Ding

Learning to Speed Up Structured Output Prediction

Xingyuan Pan, Vivek Srikumar

Reinforcement Learning with Function-Valued Action Spaces for Partial Differential Equation Control

Yangchen Pan, Amir-massoud Farahmand, Martha White, Saleh Nabi, Piyush Grover

Tree Edit Distance Learning via Adaptive Symbol Embeddings

Benjamin Paaßen, Claudio Gallicchio, Alessio Micheli, Barbara Hammer

Learning Compact Neural Networks with Regularization

Samet Oymak

Analyzing Uncertainty in Neural Machine Translation

Myle Ott, Michael Auli, David Grangier, Marc’Aurelio Ranzato

Efficient First-Order Algorithms for Adaptive Signal Denoising

Dmitrii Ostrovskii, Zaïd Harchaoui

Autoregressive Quantile Networks for Generative Modeling

Georg Ostrovski, Will Dabney, Rémi Munos

Learning Localized Spatio-Temporal Models From Streaming Data

Muhammad Osama, Dave Zachariah, Thomas Schön

Parallel WaveNet: Fast High-Fidelity Speech Synthesis

Aäron Oord, Yazhe Li, Igor Babuschkin, Karen Simonyan, Oriol Vinyals

Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches

Simon Olofsson, Marc Deisenroth, Ruth Misener

Transformation Autoregressive Networks

Junier Oliva, Avinava Dubey, Manzil Zaheer, Barnabás Póczos, Ruslan Salakhutdinov

A probabilistic framework for multi-view feature learning with many-to-many associations via neural networks

Akifumi Okuno, Tetsuya Hada, Hidetoshi Shimodaira

Self-Imitation Learning

Junhyuk Oh, Yijie Guo, Satinder Singh, Honglak Lee

BOCK : Bayesian Optimization with Cylindrical Kernels

Changyong Oh, Efstratios Gavves, Max Welling

Learning in Reproducing Kernel Kreı̆n Spaces

Dino Oglic, Thomas Gaertner

Is Generator Conditioning Causally Related to GAN Performance?

Augustus Odena, Jacob Buckman, Catherine Olsson, Tom Brown, Christopher Olah

The Uncertainty Bellman Equation and Exploration

Brendan O’Donoghue, Ian Osband, Rémi Munos, Vlad Mnih

Beyond 1/2-Approximation for Submodular Maximization on Massive Data Streams

Ashkan Norouzi-Fard, Jakub Tarnawski, Slobodan Mitrovic, Amir Zandieh, Aidasadat Mousavifar

Functional Gradient Boosting based on Residual Network Perception

Atsushi Nitanda, Taiji Suzuki

A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations

Weili Nie, Yang Zhang, Ankit Patel

SparseMAP: Differentiable Sparse Structured Inference

Vlad Niculae, Andre Martins, Mathieu Blondel, Claire Cardie

State Space Gaussian Processes with Non-Gaussian Likelihood

Hannes Nickisch, Arno Solin, Alexander Grigorevskiy

Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry

Maximillian Nickel, Douwe Kiela

On Learning Sparsely Used Dictionaries from Incomplete Samples

Thanh Nguyen, Akshay Soni, Chinmay Hegde

Active Testing: An Efficient and Robust Framework for Estimating Accuracy

Phuc Nguyen, Deva Ramanan, Charless Fowlkes

SGD and Hogwild! Convergence Without the Bounded Gradients Assumption

Lam Nguyen, PHUONG HA NGUYEN, Marten Dijk, Peter Richtarik, Katya Scheinberg

Neural Networks Should Be Wide Enough to Learn Disconnected Decision Regions

Quynh Nguyen, Mahesh Chandra Mukkamala, Matthias Hein

Optimization Landscape and Expressivity of Deep CNNs

Quynh Nguyen, Matthias Hein

Mitigating Bias in Adaptive Data Gathering via Differential Privacy

Seth Neel, Aaron Roth

Stochastic Proximal Algorithms for AUC Maximization

Michael Natole, Yiming Ying, Siwei Lyu

Nearly Optimal Robust Subspace Tracking

Praneeth Narayanamurthy, Namrata Vaswani

Smoothed Action Value Functions for Learning Gaussian Policies

Ofir Nachum, Mohammad Norouzi, George Tucker, Dale Schuurmans

Fitting New Speakers Based on a Short Untranscribed Sample

Eliya Nachmani, Adam Polyak, Yaniv Taigman, Lior Wolf

On the Relationship between Data Efficiency and Error for Uncertainty Sampling

Stephen Mussmann, Percy Liang

Rapid Adaptation with Conditionally Shifted Neurons

Tsendsuren Munkhdalai, Xingdi Yuan, Soroush Mehri, Adam Trischler

Kernelized Synaptic Weight Matrices

Lorenz Muller, Julien Martel, Giacomo Indiveri

Dropout Training, Data-dependent Regularization, and Generalization Bounds

Wenlong Mou, Yuchen Zhou, Jun Gao, Liwei Wang

WHInter: A Working set algorithm for High-dimensional sparse second order Interaction models

Marine Le Morvan, Jean-philippe Vert

DICOD: Distributed Convolutional Coordinate Descent for Convolutional Sparse Coding

Thomas Moreau, Laurent Oudre, Nicolas Vayatis

Decentralized Submodular Maximization: Bridging Discrete and Continuous Settings

Aryan Mokhtari, Hamed Hassani, Amin Karbasi

The Hierarchical Adaptive Forgetting Variational Filter

Vincent Moens

Data Summarization at Scale: A Two-Stage Submodular Approach

Marko Mitrovic, Ehsan Kazemi, Morteza Zadimoghaddam, Amin Karbasi

A Delay-tolerant Proximal-Gradient Algorithm for Distributed Learning

Konstantin Mishchenko, Franck Iutzeler, Jérôme Malick, Massih-Reza Amini

Differentiable Abstract Interpretation for Provably Robust Neural Networks

Matthew Mirman, Timon Gehr, Martin Vechev

Training Neural Machines with Trace-Based Supervision

Matthew Mirman, Dimitar Dimitrov, Pavle Djordjevic, Timon Gehr, Martin Vechev

Differentiable plasticity: training plastic neural networks with backpropagation

Thomas Miconi, Kenneth Stanley, Jeff Clune

One-Shot Segmentation in Clutter

Claudio Michaelis, Matthias Bethge, Alexander Ecker

On the Implicit Bias of Dropout

Poorya Mianjy, Raman Arora, René Vidal

Stochastic PCA with ℓ_2 and ℓ_1 Regularization

Poorya Mianjy, Raman Arora

The Hidden Vulnerability of Distributed Learning in Byzantium

El-Mahdi El-Mhamdi, Rachid Guerraoui, Sébastien Rouault

Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing—and Back

Elliot Meyerson, Risto Miikkulainen

prDeep: Robust Phase Retrieval with a Flexible Deep Network

Christopher Metzler, Phillip Schniter, Ashok Veeraraghavan, Richard Baraniuk

Configurable Markov Decision Processes

Alberto Maria Metelli, Mirco Mutti, Marcello Restelli

Which Training Methods for GANs do actually Converge?

Lars Mescheder, Andreas Geiger, Sebastian Nowozin

Ranking Distributions based on Noisy Sorting

Adil El Mesaoudi-Paul, Eyke Hüllermeier, Róbert Busa-Fekete

Differentiable Dynamic Programming for Structured Prediction and Attention

Arthur Mensch, Mathieu Blondel

Bounds on the Approximation Power of Feedforward Neural Networks

Mohammad Mehrabi, Aslan Tchamkerten, MANSOOR YOUSEFI

Optimization, fast and slow: optimally switching between local and Bayesian optimization

Mark McLeod, Stephen Roberts, Michael A. Osborne

Bayesian Model Selection for Change Point Detection and Clustering

Othmane Mazhar, Cristian Rojas, Carlo Fischione, Mohammad Reza Hesamzadeh

Fast Approximate Spectral Clustering for Dynamic Networks

Lionel Martin, Andreas Loukas, Pierre Vandergheynst

Streaming Principal Component Analysis in Noisy Setting

Teodor Vanislavov Marinov, Poorya Mianjy, Raman Arora

Iterative Amortized Inference

Joe Marino, Yisong Yue, Stephan Mandt

An Efficient, Generalized Bellman Update For Cooperative Inverse Reinforcement Learning

Dhruv Malik, Malayandi Palaniappan, Jaime Fisac, Dylan Hadfield-Menell, Stuart Russell

Learning Adversarially Fair and Transferable Representations

David Madras, Elliot Creager, Toniann Pitassi, Richard Zemel

Orthogonal Machine Learning: Power and Limitations

Lester Mackey, Vasilis Syrgkanis, Ilias Zadik

Approximate message passing for amplitude based optimization

Junjie Ma, Ji Xu, Arian Maleki

Dimensionality-Driven Learning with Noisy Labels

Xingjun Ma, Yisen Wang, Michael E. Houle, Shuo Zhou, Sarah Erfani

Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval and Matrix Completion

Cong Ma, Kaizheng Wang, Yuejie Chi, Yuxin Chen

Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers

Yao Ma, Alexander Olshevsky, Csaba Szepesvári, Venkatesh Saligrama

The Power of Interpolation: Understanding the Effectiveness of SGD in Modern Over-parametrized Learning

SIYUAN MA, Raef Bassily, Mikhail Belkin

Celer: a Fast Solver for the Lasso with Dual Extrapolation

Mathurin Massias, Alexandre Gramfort, Joseph Salmon

Batch Bayesian Optimization via Multi-objective Acquisition Ensemble for Automated Analog Circuit Design

Wenlong Lyu, Fan Yang, Changhao Yan, Dian Zhou, Xuan Zeng

Competitive Caching with Machine Learned Advice

Thodoris Lykouris, Sergei Vassilvtiskii

End-to-end Active Object Tracking via Reinforcement Learning

Wenhan Luo, peng sun, Fangwei Zhong, Wei Liu, Tong Zhang

Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations

Yiping Lu, Aoxiao Zhong, Quanzheng Li, Bin Dong

Structured Variationally Auto-encoded Optimization

Xiaoyu Lu, Javier Gonzalez, Zhenwen Dai, Neil D. Lawrence

Accelerating Greedy Coordinate Descent Methods

Haihao Lu, Robert Freund, Vahab Mirrokni

The Edge Density Barrier: Computational-Statistical Tradeoffs in Combinatorial Inference

Hao Lu, Yuan Cao, Zhuoran Yang, Junwei Lu, Han Liu

Spectrally Approximating Large Graphs with Smaller Graphs

Andreas Loukas, Pierre Vandergheynst

Constraining the Dynamics of Deep Probabilistic Models

Marco Lorenzi, Maurizio Filippone

Error Estimation for Randomized Least-Squares Algorithms via the Bootstrap

Miles Lopes, Shusen Wang, Michael Mahoney

PDE-Net: Learning PDEs from Data

Zichao Long, Yiping Lu, Xianzhong Ma, Bin Dong

On Matching Pursuit and Coordinate Descent

Francesco Locatello, Anant Raj, Sai Praneeth Karimireddy, Gunnar Raetsch, Bernhard Schölkopf

Fast Stochastic AUC Maximization with $O(1/n)$-Convergence Rate

Mingrui Liu, Xiaoxuan Zhang, Zaiyi Chen, Xiaoyu Wang, Tianbao Yang

Fast Variance Reduction Method with Stochastic Batch Size

Xuanqing Liu, Cho-jui Hsieh

Open Category Detection with PAC Guarantees

Si Liu, Risheek Garrepalli, Thomas Dietterich, Alan Fern, Dan Hendrycks

A Two-Step Computation of the Exact GAN Wasserstein Distance

Huidong Liu, Xianfeng Gu, Dimitris Samaras

Delayed Impact of Fair Machine Learning

Lydia T. Liu, Sarah Dean, Esther Rolf, Max Simchowitz, Moritz Hardt

Towards Black-box Iterative Machine Teaching

Weiyang Liu, Bo Dai, Xingguo Li, Zhen Liu, James Rehg

Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression

Haitao Liu, Jianfei Cai, Yi Wang, Yew Soon Ong