ICML

Stein Variational Gradient Descent Without Gradient

Jun Han, Qiang Liu

Comments ICML 2018

Path-Level Network Transformation for Efficient Architecture Search

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

Comments ICML 2018

Asynchronous Stochastic Quasi-Newton MCMC for Non-Convex Optimization

Umut Şimşekli, Çağatay Yıldız, Thanh Huy Nguyen, Gaël Richard, A. Taylan Cemgil

Comments Published in the International Conference on Machine Learning (ICML 2018)

Adversarial Attack on Graph Structured Data

Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, Le Song

Comments to appear in ICML 2018

K-Beam Minimax: Efficient Optimization for Deep Adversarial Learning

Jihun Hamm, Yung-Kyun Noh

Comments Accepted for ICML 2018

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

Borja Balle, Yu-Xiang Wang

Comments To appear at the 35th International Conference on Machine Learning (ICML), 2018

Online Convolutional Sparse Coding with Sample-Dependent Dictionary

Yaqing Wang, Quanming Yao, James T. Kwok, Lionel M. Ni

Comments Accepted by ICML-2018

Hyperbolic Entailment Cones for Learning Hierarchical Embeddings

Octavian-Eugen Ganea, Gary Bécigneul, Thomas Hofmann

Comments International Conference on Machine Learning (ICML) 2018

Stochastic Wasserstein Barycenters

Sebastian Claici, Edward Chien, Justin Solomon

Comments ICML 2018

Large-Scale Sparse Inverse Covariance Estimation via Thresholding and Max-Det Matrix Completion

Richard Y. Zhang, Salar Fattahi, Somayeh Sojoudi

Comments 35-th International Conference on Machine Learning (ICML 2018)

First Order Generative Adversarial Networks

Calvin Seward, Thomas Unterthiner, Urs Bergmann, Nikolay Jetchev, Sepp Hochreiter

Comments Accepted to 35th International Conference on Machine Learning (ICML). Code to reproduce experiments is available https://github.com/zalandoresearch/first_order_gan

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

Pavel Dvurechensky, Alexander Gasnikov, Alexey Kroshnin

Comments Accepted for ICML 2018

Augment and Reduce: Stochastic Inference for Large Categorical Distributions

Francisco J. R. Ruiz, Michalis K. Titsias, Adji B. Dieng, David M. Blei

Comments 11 pages, 2 figures

Journal ref Francisco J. R. Ruiz, Michalis K. Titsias, Adji B. Dieng, and David M. Blei. Augment and Reduce: Stochastic Inference for Large Categorical Distributions. International Conference on Machine Learning. Stockholm (Sweden), July 2018

Learning Compact Neural Networks with Regularization

Samet Oymak

Comments ICML 2018, 46 pages, 11 figures

Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks

Brenden M. Lake, Marco Baroni

Comments Published at the 35th International Conference on Machine Learning (ICML 2018)

Journal ref Lake, B. M. and Baroni, M. (2018). Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks. International Conference on Machine Learning (ICML)

Synthesizing Robust Adversarial Examples

Anish Athalye, Logan Engstrom, Andrew Ilyas, Kevin Kwok

Comments ICML 2018

Mitigating Bias in Adaptive Data Gathering via Differential Privacy

Seth Neel, Aaron Roth

Comments Conference version appears in ICML 2018

Learning Kolmogorov Models for Binary Random Variables

Hadi Ghauch, Mikael Skoglund, Hossein Shokri-Ghadikolaei, Carlo Fischione, Ali H. Sayed

Comments 9 pages, accecpted to ICML 2018: Workshop on Nonconvex Optimization

Adversarial Regression with Multiple Learners

Liang Tong, Sixie Yu, Scott Alfeld, Yevgeniy Vorobeychik

Comments Accepted by ICML'18

Causal Bandits with Propagating Inference

Akihiro Yabe, Daisuke Hatano, Hanna Sumita, Shinji Ito, Naonori Kakimura, Takuro Fukunaga, Ken-ichi Kawarabayashi

Comments To appear in International Conference on Machine Learning 2018

Improving the Privacy and Accuracy of ADMM-Based Distributed Algorithms

Xueru Zhang, Mohammad Mahdi Khalili, Mingyan Liu

Comments accepted to 35th International Conference on Machine Learning

Spatio-temporal Bayesian On-line Changepoint Detection with Model Selection

Jeremias Knoblauch, Theodoros Damoulas

Comments 10 pages, 7f figures, to appear in Proceedings of the 35th International Conference on Machine Learning 2018

The Mechanics of n-Player Differentiable Games

David Balduzzi, Sebastien Racaniere, James Martens, Jakob Foerster, Karl Tuyls, Thore Graepel

Comments ICML 2018, final version

Journal ref PMLR volume 80, 2018

Neural Relational Inference for Interacting Systems

Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard Zemel

Comments ICML (2018). Code available under https://github.com/ethanfetaya/NRI

Let's be Honest: An Optimal No-Regret Framework for Zero-Sum Games

Ehsan Asadi Kangarshahi, Ya-Ping Hsieh, Mehmet Fatih Sahin, Volkan Cevher

Comments Proceedings of the 35th International Conference on Machine Learning

Explicit Inductive Bias for Transfer Learning with Convolutional Networks

Xuhong Li, Yves Grandvalet, Franck Davoine

Comments Accepted at ICML 2018

Competitive Multi-agent Inverse Reinforcement Learning with Sub-optimal Demonstrations

Xingyu Wang, Diego Klabjan

Comments 31 pages, to be presented at ICML 2018

SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation

Bo Dai, Albert Shaw, Lihong Li, Lin Xiao, Niao He, Zhen Liu, Jianshu Chen, Le Song

Comments 28 pages, 13 figures. To appear at the 35th International Conference on Machine Learning (ICML 2018)

Fast Information-theoretic Bayesian Optimisation

Binxin Ru, Mark McLeod, Diego Granziol, Michael A. Osborne

Comments Main Paper: 9 pages, 6 figures, 2 tables; Accepted by ICML 2018

Optimization Landscape and Expressivity of Deep CNNs

Quynh Nguyen, Matthias Hein

Comments Accepted at ICML 2018