ICML

Reviving and Improving Recurrent Back-Propagation

Renjie Liao, Yuwen Xiong, Ethan Fetaya, Lisa Zhang, KiJung Yoon, Xaq Pitkow, Raquel Urtasun, Richard Zemel

Comments International Conference on Machine Learning

3D Deformable Convolutions for MRI classification

Marina Pominova, Ekaterina Kondrateva, Maksim Sharaev, Sergey Pavlov, Alexander Bernstein, Evgeny Burnaev

Comments Accepted to IEEE International Conference on Machine Learning and Applications (ICMLA 2019)

Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs

Andrea Zanette, Emma Brunskill

Journal ref International Conference on Machine Learning, 2018

A Narration-based Reward Shaping Approach using Grounded Natural Language Commands

Nicholas Waytowich, Sean L. Barton, Vernon Lawhern, Garrett Warnell

Comments Presented at the Imitation, Intent and Interaction (I3) workshop, ICML 2019. arXiv admin note: substantial text overlap with arXiv:1906.02671

HOList: An Environment for Machine Learning of Higher-Order Theorem Proving

Kshitij Bansal, Sarah M. Loos, Markus N. Rabe, Christian Szegedy, Stewart Wilcox

Comments Accepted at ICML 2019

Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds

Andrea Zanette, Emma Brunskill

Comments Bug fixes

Journal ref International Conference on Machine Learning 2019

Two Phase Authentication and VPN Based Secured Communication for IoT Home Networks

Md Masuduzzaman, Ashik Mahmud, Anik Islam, Md Mofijul Islam

Comments Paper has been accepted in 1st International Conference on Machine Learning, Image Processing, Network Security and Data Sciences 3-4 March, 2019, NIT Kurukshetra, INDIA. It's in press now

BOCK : Bayesian Optimization with Cylindrical Kernels

ChangYong Oh, Efstratios Gavves, Max Welling

Comments 10 pages, 5 figures, 5 tables, 1 algorithm

Journal ref Proceedings of the 35th International Conference on Machine Learning, Stockholm, Sweden, PMLR 80, 2018

Improved Zeroth-Order Variance Reduced Algorithms and Analysis for Nonconvex Optimization

Kaiyi Ji, Zhe Wang, Yi Zhou, Yingbin Liang

Comments Published in ICML 2019

Not All Samples Are Created Equal: Deep Learning with Importance Sampling

Angelos Katharopoulos, François Fleuret

Comments Accepted at ICML 2018 (short oral)

Non-Monotonic Sequential Text Generation

Sean Welleck, Kianté Brantley, Hal Daumé, Kyunghyun Cho

Comments ICML 2019

Adversarial Time-to-Event Modeling

Paidamoyo Chapfuwa, Chenyang Tao, Chunyuan Li, Courtney Page, Benjamin Goldstein, Lawrence Carin, Ricardo Henao

Comments Published in ICML 2018; Code: https://github.com/paidamoyo/adversarial_time_to_event

Journal ref Proceedings of the 35th International Conference on Machine Learning, PMLR 80:735-744, 2018

Machine Learning for AC Optimal Power Flow

Neel Guha, Zhecheng Wang, Matt Wytock, Arun Majumdar

Comments 3 pages, 2 tables. Presented at the Climate Change Workshop at ICML 2019

Using Pre-Training Can Improve Model Robustness and Uncertainty

Dan Hendrycks, Kimin Lee, Mantas Mazeika

Comments ICML 2019. PyTorch code here: https://github.com/hendrycks/pre-training Figure 3 updated

Performing Deep Recurrent Double Q-Learning for Atari Games

Felipe Moreno-Vera

Comments Accepted paper on LatinXinAI Workshop co-located with the International Conference on Machine Learning (ICML) 2019

Coordination of PV Smart Inverters Using Deep Reinforcement Learning for Grid Voltage Regulation

Changfu Li, Chenrui Jin, Ratnesh Sharma

Comments 18th IEEE International Conference on Machine Learning and Applications - ICMLA 2019

Dealing with Stochasticity in Biological ODE Models

Hamda Ajmal, Michael Madden, Catherine Enright

Comments 5 pages. In Workshop on Computational Biology (WCB) 2017, co-located with the 34th International Conference on Machine Learning (ICML). arXiv admin note: text overlap with arXiv:1910.04895

Learning Important Features Through Propagating Activation Differences

Avanti Shrikumar, Peyton Greenside, Anshul Kundaje

Comments Updated to include changes present in the ICML camera-ready paper, and other small corrections

Journal ref PMLR 70:3145-3153, 2017

On the Limitations of Representing Functions on Sets

Edward Wagstaff, Fabian B. Fuchs, Martin Engelcke, Ingmar Posner, Michael Osborne

Comments Published at the International Conference on Machine Learning (2019)

Enhancing Decision Tree based Interpretation of Deep Neural Networks through L1-Orthogonal Regularization

Nina Schaaf, Marco F. Huber, Johannes Maucher

Comments 8 pages, 18th IEEE International Conference on Machine Learning and Applications (ICMLA) 2019

Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems

Geoffrey Roeder, Paul K. Grant, Andrew Phillips, Neil Dalchau, Edward Meeds

Comments Published in "Proceedings of Machine Learning Research, Volume 97: International Conference on Machine Learning, 9-15 June 2019, Long Beach, California, USA"

Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variables

Friso H. Kingma, Pieter Abbeel, Jonathan Ho

Comments Accepted to ICML 2019

libGroomRL: Reinforcement Learning for Jets

Stefano Carrazza, Frédéric A. Dreyer

Comments 6 pages, 3 figures, conference proceedings of ACAT and ICML. arXiv admin note: substantial text overlap with arXiv:1903.09644

Language-Agnostic Syllabification with Neural Sequence Labeling

Jacob Krantz, Maxwell Dulin, Paul De Palma

Comments Accepted as full paper for presentation at the 18th IEEE International Conference on Machine Learning and Applications (ICMLA 2019). 7 pages

Towards Learning of Filter-Level Heterogeneous Compression of Convolutional Neural Networks

Yochai Zur, Chaim Baskin, Evgenii Zheltonozhskii, Brian Chmiel, Itay Evron, Alex M. Bronstein, Avi Mendelson

Comments Accepted to ICML Workshop on AutoML 2019

Orthogonal Random Forest for Causal Inference

Miruna Oprescu, Vasilis Syrgkanis, Zhiwei Steven Wu

Comments This paper appeared in the Proceedings of the 36th International Conference on Machine Learning

Visualizing How Embeddings Generalize

Xiaotong Liu, Hong Xuan, Zeyu Zhang, Abby Stylianou, Robert Pless

Comments 8 pages,4 figures, published in ICML workshop:Understanding and Improving Generalization in Deep Learning

Exploring the Landscape of Spatial Robustness

Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, Aleksander Madry

Comments ICML 2019. Presented in NIPS 2017 Workshop on Machine Learning and Computer Security as "A Rotation and a Translation Suffice: Fooling CNNs with Simple Transformations."

Dropping Convexity for More Efficient and Scalable Online Multiview Learning

Zhehui Chen, Lin F. Yang, Chris J. Li, Tuo Zhao

Comments A preliminary version appears in ICML 2017

Towards Understanding the Importance of Noise in Training Neural Networks

Mo Zhou, Tianyi Liu, Yan Li, Dachao Lin, Enlu Zhou, Tuo Zhao

Comments International Conference on Machine Learning (ICML), 2019