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Decentralized Competing Bandits in Non-Stationary Matching Markets



Avishek Ghosh , Abishek Sankararaman , Kannan Ramchandran , Tara Javidi , Arya Mazumdar


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On Learning Mixture of Linear Regressions in the Non-Realizable Setting



Avishek Ghosh , Arya Mazumdar , Soumyabrata Pal , Rajat Sen

* To appear in ICML 2022 

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Breaking the $\sqrt{T}$ Barrier: Instance-Independent Logarithmic Regret in Stochastic Contextual Linear Bandits



Avishek Ghosh , Abishek Sankararaman

* To appear in ICML 2022 

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



Avishek Ghosh , Sayak Ray Chowdhury , Kannan Ramchandran

* 24 pages 

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Model Selection for Generic Contextual Bandits



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



Avishek Ghosh , Abishek Sankararaman , Kannan Ramchandran

* 25 pages, 8 figures 

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LocalNewton: Reducing Communication Bottleneck for Distributed Learning



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



Avishek Ghosh , Raj Kumar Maity , Arya Mazumdar , Kannan Ramchandran


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Distributed Newton Can Communicate Less and Resist Byzantine Workers



Avishek Ghosh , Raj Kumar Maity , Arya Mazumdar


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Problem-Complexity Adaptive Model Selection for Stochastic Linear Bandits



Avishek Ghosh , Abishek Sankararaman , Kannan Ramchandran

* 24 pages, 8 figures 

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