ICML

Deriving Neural Architectures from Sequence and Graph Kernels

Tao Lei, Wengong Jin, Regina Barzilay, Tommi Jaakkola

Comments extended version of ICML 2017 camera ready

Efficient Online Minimization for Low-Rank Subspace Clustering

Jie Shen, Ping Li, Huan Xu

Comments Short version accepted to ICML 2016

Learning Independent Features with Adversarial Nets for Non-linear ICA

Philemon Brakel, Yoshua Bengio

Comments A preliminary version of this work was presented at the ICML 2017 workshop on implicit models

Learnable Explicit Density for Continuous Latent Space and Variational Inference

Chin-Wei Huang, Ahmed Touati, Laurent Dinh, Michal Drozdzal, Mohammad Havaei, Laurent Charlin, Aaron Courville

Comments 2 figures, 5 pages, submitted to ICML Principled Approaches to Deep Learning workshop

Dilated Convolutions for Modeling Long-Distance Genomic Dependencies

Ankit Gupta, Alexander M. Rush

Comments Presented at the ICML Workshop on Computational Biology in Sydney, Australia in August 2017

Orthogonalized ALS: A Theoretically Principled Tensor Decomposition Algorithm for Practical Use

Vatsal Sharan, Gregory Valiant

Comments Minor updates to presentation. Appears in ICML'17

Neural Optimizer Search with Reinforcement Learning

Irwan Bello, Barret Zoph, Vijay Vasudevan, Quoc V. Le

Comments ICML 2017 Conference paper

Confident Multiple Choice Learning

Kimin Lee, Changho Hwang, KyoungSoo Park, Jinwoo Shin

Comments Accepted in ICML 2017

Adaptive Neural Networks for Efficient Inference

Tolga Bolukbasi, Joseph Wang, Ofer Dekel, Venkatesh Saligrama

Journal ref Proceedings of the 34th International Conference on Machine Learning, PMLR 70:527-536, 2017

Learning Robust Video Synchronization without Annotations

Patrick Wieschollek, Ido Freeman, Hendrik P. A. Lensch

Comments International Conference On Machine Learning And Applications (ICMLA 2017)

A constrained L1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models

Beilun Wang, Ritambhara Singh, Yanjun Qi

Comments Extended Journal Version / Previously @ ICML 2016 comp. bio workshop

Leveraging Union of Subspace Structure to Improve Constrained Clustering

John Lipor, Laura Balzano

Comments 11 pages, 8 figures

Journal ref Proceedings of the 34th International Conference on Machine Learning, in PMLR 70:2130-2139 (2017)

Deciding How to Decide: Dynamic Routing in Artificial Neural Networks

Mason McGill, Pietro Perona

Comments ICML 2017. Code at https://github.com/MasonMcGill/multipath-nn Video abstract at https://youtu.be/NHQsDaycwyQ

Learning Texture Manifolds with the Periodic Spatial GAN

Urs Bergmann, Nikolay Jetchev, Roland Vollgraf

Comments Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia, 2017. JMLR: W&CP. Copyright 2017 by the author(s)

Learning the Structure of Generative Models without Labeled Data

Stephen H. Bach, Bryan He, Alexander Ratner, Christopher Ré

Journal ref Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia, PMLR 70, 2017

A Variational Analysis of Stochastic Gradient Algorithms

Stephan Mandt, Matthew D. Hoffman, David M. Blei

Comments 8 pages, 3 figures

Journal ref International Conference on Machine Learning (ICML 2016), p. 354--363

Learned Optimizers that Scale and Generalize

Olga Wichrowska, Niru Maheswaranathan, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Nando de Freitas, Jascha Sohl-Dickstein

Comments Final ICML paper after reviewer suggestions

RNN-based Early Cyber-Attack Detection for the Tennessee Eastman Process

Pavel Filonov, Fedor Kitashov, Andrey Lavrentyev

Comments ICML 2017 Time Series Workshop, Sydney, Australia, 2017

SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient

Lam M. Nguyen, Jie Liu, Katya Scheinberg, Martin Takáč

Journal ref Proceedings of the 34th International Conference on Machine Learning, PMLR 70:2613-2621, 2017

When can Multi-Site Datasets be Pooled for Regression? Hypothesis Tests, $\ell_2$-consistency and Neuroscience Applications

Hao Henry Zhou, Yilin Zhang, Vamsi K. Ithapu, Sterling C. Johnson, Grace Wahba, Vikas Singh

Comments 34th International Conference on Machine Learning

Active Preference Learning for Personalized Portfolio Construction

Kevin Tee, Michael McCourt, Ruben Martinez-Cantin, Ian Dewancker, Frank Liu

Comments 4 pages, 2 figures, 1 algorithm, ICML Human in the Loop workshop

On Relaxing Determinism in Arithmetic Circuits

Arthur Choi, Adnan Darwiche

Comments In Proceedings of the Thirty-fourth International Conference on Machine Learning (ICML)

Magnetic Hamiltonian Monte Carlo

Nilesh Tripuraneni, Mark Rowland, Zoubin Ghahramani, Richard Turner

Comments 34th International Conference on Machine Learning (ICML 2017)

A Deep Learning Approach for Joint Video Frame and Reward Prediction in Atari Games

Felix Leibfried, Nate Kushman, Katja Hofmann

Comments Presented at the ICML 2017 Workshop on Principled Approaches to Deep Learning, Sydney, Australia, 2017

Deep Transfer Learning with Joint Adaptation Networks

Mingsheng Long, Han Zhu, Jianmin Wang, Michael I. Jordan

Comments 34th International Conference on Machine Learning

Towards Visual Explanations for Convolutional Neural Networks via Input Resampling

Benjamin J. Lengerich, Sandeep Konam, Eric P. Xing, Stephanie Rosenthal, Manuela Veloso

Comments Presented at ICML 2017 Workshop on Visualization for Deep Learning

Benchmark Environments for Multitask Learning in Continuous Domains

Peter Henderson, Wei-Di Chang, Florian Shkurti, Johanna Hansen, David Meger, Gregory Dudek

Comments Accepted at Lifelong Learning: A Reinforcement Learning Approach Workshop @ ICML, Sydney, Australia, 2017

Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control

Riashat Islam, Peter Henderson, Maziar Gomrokchi, Doina Precup

Comments Accepted to Reproducibility in Machine Learning Workshop, ICML'17

Gram-CTC: Automatic Unit Selection and Target Decomposition for Sequence Labelling

Hairong Liu, Zhenyao Zhu, Xiangang Li, Sanjeev Satheesh

Comments Published at ICML 2017

DeepBach: a Steerable Model for Bach Chorales Generation

Gaëtan Hadjeres, François Pachet, Frank Nielsen

Comments 10 pages, ICML2017 version

Journal ref Proceedings of the 34th International Conference on Machine Learning, PMLR 70:1362-1371, 2017