ICML

Greedy Column Subset Selection: New Bounds and Distributed Algorithms

Jason Altschuler, Aditya Bhaskara, Gang Fu, Vahab Mirrokni, Afshin Rostamizadeh, Morteza Zadimoghaddam

Comments to appear in International Conference on Machine Learning (ICML) 2016

Journal ref Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2539-2548, 2016

RRL: Resnet as representation for Reinforcement Learning

Rutav Shah, Vikash Kumar

Comments Published at ICML 2021

Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices

Evan Zheran Liu, Aditi Raghunathan, Percy Liang, Chelsea Finn

Comments International Conference on Machine Learning (ICML), 2021

MetaCURE: Meta Reinforcement Learning with Empowerment-Driven Exploration

Jin Zhang, Jianhao Wang, Hao Hu, Tong Chen, Yingfeng Chen, Changjie Fan, Chongjie Zhang

Journal ref In International Conference on Machine Learning (2021, pp. 12600-12610). PMLR

Can we have it all? On the Trade-off between Spatial and Adversarial Robustness of Neural Networks

Sandesh Kamath, Amit Deshpande, K V Subrahmanyam, Vineeth N Balasubramanian

Comments Accepted NeurIPS 2021. Preliminary version consisting early experimental results was presented in ICML 2018 Workshop on "Towards learning with limited labels: Equivariance, Invariance,and Beyond" as "Understanding Adversarial Robustness of Symmetric Networks"

RATT: Leveraging Unlabeled Data to Guarantee Generalization

Saurabh Garg, Sivaraman Balakrishnan, J. Zico Kolter, Zachary C. Lipton

Comments ICML 2021 (Long Talk)

Large Scale Private Learning via Low-rank Reparametrization

Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, Tie-Yan Liu

Comments Published as a conference paper in International Conference on Machine Learning (ICML 2021). Source code available at https://github.com/dayu11/Differentially-Private-Deep-Learning

AlphaD3M: Machine Learning Pipeline Synthesis

Iddo Drori, Yamuna Krishnamurthy, Remi Rampin, Raoni de Paula Lourenco, Jorge Piazentin Ono, Kyunghyun Cho, Claudio Silva, Juliana Freire

Comments ICML 2018 AutoML Workshop

A Bayesian Approach to Invariant Deep Neural Networks

Nikolaos Mourdoukoutas, Marco Federici, Georges Pantalos, Mark van der Wilk, Vincent Fortuin

Comments 8 pages, 3 figures, To be published in ICML UDL 2021

Evolving-Graph Gaussian Processes

David Blanco-Mulero, Markus Heinonen, Ville Kyrki

Comments 12 pages, 5 figures. Accepted for publication at ICML 2021 Time Series Workshop (TSW)

Composable Sketches for Functions of Frequencies: Beyond the Worst Case

Edith Cohen, Ofir Geri, Rasmus Pagh

Comments Full version of a paper from ICML 2020. Python implementation available as part of the supplemental material accompanying the ICML publication

Detecting Rewards Deterioration in Episodic Reinforcement Learning

Ido Greenberg, Shie Mannor

Comments ICML 2021

Randomized Exploration for Reinforcement Learning with General Value Function Approximation

Haque Ishfaq, Qiwen Cui, Viet Nguyen, Alex Ayoub, Zhuoran Yang, Zhaoran Wang, Doina Precup, Lin F. Yang

Comments 32 page, 5 figures, in Proceedings of the 38th International Conference on Machine Learning, PMLR 139, 2021

Towards Practical Mean Bounds for Small Samples

My Phan, Philip S. Thomas, Erik Learned-Miller

Comments This is an extended work of our ICML 2021 paper "Towards Practical Mean Bounds for Small Samples"

On the Difficulty of Unbiased Alpha Divergence Minimization

Tomas Geffner, Justin Domke

Comments ICML 2021

Journal ref ICML 2021

Optimal Model Averaging: Towards Personalized Collaborative Learning

Felix Grimberg, Mary-Anne Hartley, Sai P. Karimireddy, Martin Jaggi

Comments 9 pages (12 pages incl. references and appendix), 1 figure, Best Paper at International Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with ICML 2021 (FL-ICML'21) ( ICML\%202021\%20Best\%20Paper.pdf" target="_blank" rel="noopener">https://web.archive.org/web/20210908135923/http://federated-learning.org/fl-icml-2021/ICML\%202021\%20Best\%20Paper.pdf )

Fairness in Missing Data Imputation

Yiliang Zhang, Qi Long

Comments Accepted to ICML 2021 Workshop

Missing the Point: Non-Convergence in Iterative Imputation Algorithms

Hanne Ida Oberman, Stef van Buuren, Gerko Vink

Comments Presented at ICML 2020 ARTEMISS workshop. Associated GitHub repository: https://github.com/hanneoberman/MScThesis

Connection Management xAPP for O-RAN RIC: A Graph Neural Network and Reinforcement Learning Approach

Oner Orhan, Vasuki Narasimha Swamy, Thomas Tetzlaff, Marcel Nassar, Hosein Nikopour, Shilpa Talwar

Comments paper accepted to the IEEE International Conference on Machine Learning and Applications (ICMLA 2021)

Improving Robustness of Reinforcement Learning for Power System Control with Adversarial Training

Alexander Pan, Yongkyun Lee, Huan Zhang, Yize Chen, Yuanyuan Shi

Comments Published at 2021 ICML RL4RL Workshop; Submitted to 2022 PSCC

Privacy Amplification Via Bernoulli Sampling

Jacob Imola, Kamalika Chaudhuri

Comments 11 pages, 3 figures. Appeared in TPDP Workshop @ ICML 2021

Image-Level or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed Detection

Nadine Chang, Zhiding Yu, Yu-Xiong Wang, Anima Anandkumar, Sanja Fidler, Jose M. Alvarez

Comments Accepted to ICML 2021

Causal Inference in Non-linear Time-series using Deep Networks and Knockoff Counterfactuals

Wasim Ahmad, Maha Shadaydeh, Joachim Denzler

Journal ref IEEE International Conference on Machine Learning and Applications (ICMLA) 2021

The Emergence of Individuality

Jiechuan Jiang, Zongqing Lu

Comments The extended version of ICML 2021 paper

Out-of-Distribution Robustness in Deep Learning Compression

Eric Lei, Hamed Hassani, Shirin Saeedi Bidokhti

Comments Initially published at ICML-2021 ITR3 Workshop

End-to-End Natural Language Understanding Pipeline for Bangla Conversational Agents

Fahim Shahriar Khan, Mueeze Al Mushabbir, Mohammad Sabik Irbaz, MD Abdullah Al Nasim

Comments Accepted in IEEE International Conference on Machine Learning and Applications 2021 (IEEE ICMLA 2021)

SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data

Maud Lemercier, Cristopher Salvi, Thomas Cass, Edwin V. Bonilla, Theodoros Damoulas, Terry Lyons

Comments Published at ICML 2021

SoCRATES: System-on-Chip Resource Adaptive Scheduling using Deep Reinforcement Learning

Tegg Taekyong Sung, Bo Ryu

Comments This paper has been accepted for publication by 20th IEEE International Conference on Machine Learning and Applications (ICMLA 2021). The copyright is with the IEEE

Integer Programming for Causal Structure Learning in the Presence of Latent Variables

Rui Chen, Sanjeeb Dash, Tian Gao

Comments Published in ICML 2021

Message Passing Least Squares Framework and its Application to Rotation Synchronization

Yunpeng Shi, Gilad Lerman

Comments To Appear in ICML 2020 Proceedings

Journal ref International Conference on Machine Learning, 8796-8806 (2020)