ICML

An Explicitly Relational Neural Network Architecture

Murray Shanahan, Kyriacos Nikiforou, Antonia Creswell, Christos Kaplanis, David Barrett, Marta Garnelo

Comments In Proceedings ICML 2020

Neural Clustering Processes

Ari Pakman, Yueqi Wang, Catalin Mitelut, JinHyung Lee, Liam Paninski

Journal ref Published in Proceedings of the 37th International Conference on Machine Learning, Vienna, Austria, PMLR 119, 2020

Hierarchical Inter-Message Passing for Learning on Molecular Graphs

Matthias Fey, Jan-Gin Yuen, Frank Weichert

Comments Published at the Graph Representation Learning and Beyond (GRL+) Workhop at ICML 2020

Graph Neural Networks in TensorFlow and Keras with Spektral

Daniele Grattarola, Cesare Alippi

Comments ICML 2020 - GRL+ Workshop

Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning

Lingxiao Wang, Zhuoran Yang, Zhaoran Wang

Comments 31 pages; accepted to ICML; initial draft submitted in Feb, 2020

Estimating Model Uncertainty of Neural Networks in Sparse Information Form

Jongseok Lee, Matthias Humt, Jianxiang Feng, Rudolph Triebel

Comments Accepted to the Thirty-seventh International Conference on Machine Learning (ICML) 2020

Scalable Identification of Partially Observed Systems with Certainty-Equivalent EM

Kunal Menda, Jean de Becdelièvre, Jayesh K. Gupta, Ilan Kroo, Mykel J. Kochenderfer, Zachary Manchester

Comments First three authors contributed equally. Accepted at ICML 2020. Website: https://sites.google.com/stanford.edu/ceem/

How do SGD hyperparameters in natural training affect adversarial robustness?

Sandesh Kamath, Amit Deshpande, K V Subrahmanyam

Comments Preliminary version presented in ICML 2019 Workshop on "Understanding and Improving Generalization in Deep Learning" as "On Adversarial Robustness of Small vs Large Batch Training"

Two Simple Ways to Learn Individual Fairness Metrics from Data

Debarghya Mukherjee, Mikhail Yurochkin, Moulinath Banerjee, Yuekai Sun

Comments To appear in ICML 2020

Self-Supervised Prototypical Transfer Learning for Few-Shot Classification

Carlos Medina, Arnout Devos, Matthias Grossglauser

Comments Extended version of work presented at the 7th ICML Workshop on Automated Machine Learning (2020). Code available at https://github.com/indy-lab/ProtoTransfer ; 17 pages, 3 figures, 12 tables

Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training

Xuxi Chen, Wuyang Chen, Tianlong Chen, Ye Yuan, Chen Gong, Kewei Chen, Zhangyang Wang

Comments ICML 2020 accepted

Principled learning method for Wasserstein distributionally robust optimization with local perturbations

Yongchan Kwon, Wonyoung Kim, Joong-Ho Won, Myunghee Cho Paik

Comments Accepted for ICML 2020

ControlVAE: Controllable Variational Autoencoder

Huajie Shao, Shuochao Yao, Dachun Sun, Aston Zhang, Shengzhong Liu, Dongxin Liu, Jun Wang, Tarek Abdelzaher

Comments accepted by ICML2020

Journal ref 37th proceedings of ICML, 2020

The Implicit Regularization of Stochastic Gradient Flow for Least Squares

Alnur Ali, Edgar Dobriban, Ryan J. Tibshirani

Comments ICML 2020

Sample Complexity Bounds for 1-bit Compressive Sensing and Binary Stable Embeddings with Generative Priors

Zhaoqiang Liu, Selwyn Gomes, Avtansh Tiwari, Jonathan Scarlett

Comments Accepted to ICML 2020

A Notion of Harmonic Clustering in Simplicial Complexes

Stefania Ebli, Gard Spreemann

Journal ref 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)

Enhancing Simple Models by Exploiting What They Already Know

Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss

Comments Accepted to ICML 2020

Learning to Generalize from Sparse and Underspecified Rewards

Rishabh Agarwal, Chen Liang, Dale Schuurmans, Mohammad Norouzi

Comments ICML 2019

Journal ref Proceedings of the 36th International Conference on Machine Learning, PMLR 97:130-140, 2019

On the Noisy Gradient Descent that Generalizes as SGD

Jingfeng Wu, Wenqing Hu, Haoyi Xiong, Jun Huan, Vladimir Braverman, Zhanxing Zhu

Comments ICML 2020 near camera ready version

Gradient-free Online Learning in Games with Delayed Rewards

Amélie Héliou, Panayotis Mertikopoulos, Zhengyuan Zhou

Comments 26 pages, 4 figures; to appear in ICML 2020

Design Considerations for High Impact, Automated Echocardiogram Analysis

Wiebke Toussaint, Dave Van Veen, Courtney Irwin, Yoni Nachmany, Manuel Barreiro-Perez, Elena Díaz-Peláez, Sara Guerreiro de Sousa, Liliana Millán, Pedro L. Sánchez, Antonio Sánchez-Puente, Jesús Sampedro-Gómez, P. Ignacio Dorado-Díaz, Víctor Vicente-Palacios

Comments 2.5 pages, ICML 2020 Machine Learning for Global Health Workshop

Time-aware Large Kernel Convolutions

Vasileios Lioutas, Yuhong Guo

Comments Accepted by ICML 2020

Improving the Gating Mechanism of Recurrent Neural Networks

Albert Gu, Caglar Gulcehre, Tom Le Paine, Matt Hoffman, Razvan Pascanu

Comments International Conference on Machine Learning 2020

Regression Networks for Meta-Learning Few-Shot Classification

Arnout Devos, Matthias Grossglauser

Comments 7th ICML Workshop on Automated Machine Learning (2020)

Journal ref ICML Workshop on Automated Machine Learning (2020)

Learning Discrete Structures for Graph Neural Networks

Luca Franceschi, Mathias Niepert, Massimiliano Pontil, Xiao He

Comments ICML 2019, code at https://github.com/lucfra/LDS - Revision of Sec. 3

Infinite attention: NNGP and NTK for deep attention networks

Jiri Hron, Yasaman Bahri, Jascha Sohl-Dickstein, Roman Novak

Comments ICML 2020

Optimistic Policy Optimization with Bandit Feedback

Yonathan Efroni, Lior Shani, Aviv Rosenberg, Shie Mannor

Comments Accepted to ICML 2020

Bridging the Gap Between $f$-GANs and Wasserstein GANs

Jiaming Song, Stefano Ermon

Comments updated for ICML camera ready version

Approximating Stacked and Bidirectional Recurrent Architectures with the Delayed Recurrent Neural Network

Javier S. Turek, Shailee Jain, Vy Vo, Mihai Capota, Alexander G. Huth, Theodore L. Willke

Comments to be published in Proceedings of International Conference on Machine Learning 2020 (ICML)

LazyIter: A Fast Algorithm for Counting Markov Equivalent DAGs and Designing Experiments

Ali AhmadiTeshnizi, Saber Salehkaleybar, Negar Kiyavash

Comments 11 pages, 2 figures, ICML