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Model Selection with Near Optimal Rates for Reinforcement Learning with General Model Classes


Jul 13, 2021
Avishek Ghosh, Sayak Ray Chowdhury, Kannan Ramchandran

* 24 pages 

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Model Selection for Generic Contextual Bandits


Jul 07, 2021
Avishek Ghosh, Abishek Sankararaman, Kannan Ramchandran

* 40 pages, 5 figures. arXiv admin note: text overlap with arXiv:2006.02612 

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Collaborative Learning and Personalization in Multi-Agent Stochastic Linear Bandits


Jun 15, 2021
Avishek Ghosh, Abishek Sankararaman, Kannan Ramchandran

* 25 pages, 8 figures 

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LocalNewton: Reducing Communication Bottleneck for Distributed Learning


May 16, 2021
Vipul Gupta, Avishek Ghosh, Michal Derezinski, Rajiv Khanna, Kannan Ramchandran, Michael Mahoney

* To be published in Uncertainty in Artificial Intelligence (UAI) 2021 

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Escaping Saddle Points in Distributed Newton's Method with Communication efficiency and Byzantine Resilience


Mar 17, 2021
Avishek Ghosh, Raj Kumar Maity, Arya Mazumdar, Kannan Ramchandran


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Provably Breaking the Quadratic Error Compounding Barrier in Imitation Learning, Optimally


Feb 25, 2021
Nived Rajaraman, Yanjun Han, Lin F. Yang, Kannan Ramchandran, Jiantao Jiao

* 30 pages, 2 figures 

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BEAR: Sketching BFGS Algorithm for Ultra-High Dimensional Feature Selection in Sublinear Memory


Oct 26, 2020
Amirali Aghazadeh, Vipul Gupta, Alex DeWeese, O. Ozan Koyluoglu, Kannan Ramchandran


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Fast Distributed Training of Deep Neural Networks: Dynamic Communication Thresholding for Model and Data Parallelism


Oct 18, 2020
Vipul Gupta, Dhruv Choudhary, Ping Tak Peter Tang, Xiaohan Wei, Xing Wang, Yuzhen Huang, Arun Kejariwal, Kannan Ramchandran, Michael W. Mahoney

* 17 pages, 8 figures 

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FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learning


Sep 23, 2020
Swanand Kadhe, Nived Rajaraman, O. Ozan Koyluoglu, Kannan Ramchandran

* Shorter version accepted in ICML Workshop on Federated Learning, July 2020, and CCS Workshop on Privacy-Preserving Machine Learning in Practice, November 2020 

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Boundary thickness and robustness in learning models


Jul 09, 2020
Yaoqing Yang, Rajiv Khanna, Yaodong Yu, Amir Gholami, Kurt Keutzer, Joseph E. Gonzalez, Kannan Ramchandran, Michael W. Mahoney


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Problem-Complexity Adaptive Model Selection for Stochastic Linear Bandits


Jun 15, 2020
Avishek Ghosh, Abishek Sankararaman, Kannan Ramchandran

* 24 pages, 8 figures 

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An Efficient Framework for Clustered Federated Learning


Jun 07, 2020
Avishek Ghosh, Jichan Chung, Dong Yin, Kannan Ramchandran

* 20 pages, 4 figures and 1 table 

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Communication-Efficient Gradient Coding for Straggler Mitigation in Distributed Learning


May 14, 2020
Swanand Kadhe, O. Ozan Koyluoglu, Kannan Ramchandran

* Shorter version accepted in 2020 IEEE International Symposium on Information Theory (ISIT) 

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Alternating Minimization Converges Super-Linearly for Mixed Linear Regression


Apr 23, 2020
Avishek Ghosh, Kannan Ramchandran

* Accepted for publication at AISTATS, 2020 

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Communication-Efficient and Byzantine-Robust Distributed Learning


Nov 21, 2019
Avishek Ghosh, Raj Kumar Maity, Swanand Kadhe, Arya Mazumdar, Kannan Ramchandran


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Max-Affine Regression: Provable, Tractable, and Near-Optimal Statistical Estimation


Jun 21, 2019
Avishek Ghosh, Ashwin Pananjady, Adityanand Guntuboyina, Kannan Ramchandran

* The first two authors contributed equally to this work and are ordered alphabetically 

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Robust Federated Learning in a Heterogeneous Environment


Jun 16, 2019
Avishek Ghosh, Justin Hong, Dong Yin, Kannan Ramchandran

* 30 pages, 4 figures 

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Adversarially Trained Autoencoders for Parallel-Data-Free Voice Conversion


May 09, 2019
Orhan Ocal, Oguz H. Elibol, Gokce Keskin, Cory Stephenson, Anil Thomas, Kannan Ramchandran


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Gradient Coding Based on Block Designs for Mitigating Adversarial Stragglers


Apr 30, 2019
Swanand Kadhe, O. Ozan Koyluoglu, Kannan Ramchandran

* Shorter version accepted in 2019 IEEE International Symposium on Information Theory (ISIT) 

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OverSketched Newton: Fast Convex Optimization for Serverless Systems


Mar 21, 2019
Vipul Gupta, Swanand Kadhe, Thomas Courtade, Michael W. Mahoney, Kannan Ramchandran

* 27 pages, 11 figures 

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Cross-Entropy Loss and Low-Rank Features Have Responsibility for Adversarial Examples


Jan 24, 2019
Kamil Nar, Orhan Ocal, S. Shankar Sastry, Kannan Ramchandran


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Frank-Wolfe Algorithm for Exemplar Selection


Nov 06, 2018
Gary Cheng, Armin Askari, Laurent El Ghaoui, Kannan Ramchandran


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Rademacher Complexity for Adversarially Robust Generalization


Oct 29, 2018
Dong Yin, Kannan Ramchandran, Peter Bartlett


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Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning


Sep 14, 2018
Dong Yin, Yudong Chen, Kannan Ramchandran, Peter Bartlett


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Online Scoring with Delayed Information: A Convex Optimization Viewpoint


Jul 09, 2018
Avishek Ghosh, Kannan Ramchandran

* 8 pages, 4 figures 

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Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates


Mar 05, 2018
Dong Yin, Yudong Chen, Kannan Ramchandran, Peter Bartlett


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Speeding Up Distributed Machine Learning Using Codes


Jan 29, 2018
Kangwook Lee, Maximilian Lam, Ramtin Pedarsani, Dimitris Papailiopoulos, Kannan Ramchandran

* This work is published in IEEE Transactions on Information Theory and presented in part at the NIPS 2015 Workshop on Machine Learning Systems and the IEEE ISIT 2016 

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Gradient Diversity: a Key Ingredient for Scalable Distributed Learning


Jan 07, 2018
Dong Yin, Ashwin Pananjady, Max Lam, Dimitris Papailiopoulos, Kannan Ramchandran, Peter Bartlett


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Approximate Ranking from Pairwise Comparisons


Jan 04, 2018
Reinhard Heckel, Max Simchowitz, Kannan Ramchandran, Martin J. Wainwright

* AISTATS 2017 

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