Ghosh, Avishek

21 publications

TMLR 2025 Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Lokesh Nagalapatti, Pranava Singhal, Avishek Ghosh, Sunita Sarawagi

ICML 2025 Near Optimal Best Arm Identification for Clustered Bandits Yash, Avishek Ghosh, Nikhil Karamchandani

ICML 2024 Agnostic Learning of Mixed Linear Regressions with EM and AM Algorithms Avishek Ghosh, Arya Mazumdar

TMLR 2024 An Improved Federated Clustering Algorithm with Model-Based Clustering Harsh Vardhan, Avishek Ghosh, Arya Mazumdar

NeurIPSW 2024 Leveraging a Simulator for Learning Causal Representations for CATE from Post-Treatment Covariates Lokesh Nagalapatti, Pranava Singhal, Avishek Ghosh, Sunita Sarawagi

ICML 2024 PairNet: Training with Observed Pairs to Estimate Individual Treatment Effect Lokesh Nagalapatti, Pranava Singhal, Avishek Ghosh, Sunita Sarawagi

ICMLW 2023 $\texttt{FED-CURE}$: A Robust Federated Learning Algorithm with Cubic Regularized Newton Avishek Ghosh, Raj Kumar Maity, Arya Mazumdar

ICMLW 2023 A Convergent Federated Clustering Algorithm Without Initial Condition Harsh Vardhan, Avishek Ghosh, Arya Mazumdar

ICMLW 2023 Competing Bandits in Non-Stationary Matching Markets Avishek Ghosh, Abishek Sankararaman, Kannan Ramchandran, Tara Javidi, Arya Mazumdar

AISTATS 2023 Exploration in Linear Bandits with Rich Action Sets and Its Implications for Inference Debangshu Banerjee, Avishek Ghosh, Sayak Ray Chowdhury, Aditya Gopalan

ICMLW 2023 Two-Sided Bandit Learning in Fully-Decentralized Matching Markets Tejas Pagare, Avishek Ghosh

ICML 2022 Breaking the $\sqrt{T}$ Barrier: Instance-Independent Logarithmic Regret in Stochastic Contextual Linear Bandits Avishek Ghosh, Abishek Sankararaman

ECML-PKDD 2022 Model Selection in Reinforcement Learning with General Function Approximations Avishek Ghosh, Sayak Ray Chowdhury

ECML-PKDD 2022 Multi-Agent Heterogeneous Stochastic Linear Bandits Avishek Ghosh, Abishek Sankararaman, Kannan Ramchandran

ICML 2022 On Learning Mixture of Linear Regressions in the Non-Realizable Setting Soumyabrata Pal, Arya Mazumdar, Rajat Sen, Avishek Ghosh

AISTATS 2021 Problem-Complexity Adaptive Model Selection for Stochastic Linear Bandits Avishek Ghosh, Abishek Sankararaman, Ramchandran Kannan

UAI 2021 LocalNewton: Reducing Communication Rounds for Distributed Learning Vipul Gupta, Avishek Ghosh, Michał Dereziński, Rajiv Khanna, Kannan Ramchandran, Michael W. Mahoney

AISTATS 2020 Alternating Minimization Converges Super-Linearly for Mixed Linear Regression Avishek Ghosh, Ramchandran Kannan

NeurIPS 2020 An Efficient Framework for Clustered Federated Learning Avishek Ghosh, Jichan Chung, Dong Yin, Kannan Ramchandran

NeurIPS 2020 Distributed Newton Can Communicate Less and Resist Byzantine Workers Avishek Ghosh, Raj Kumar Maity, Arya Mazumdar

AAAI 2017 Misspecified Linear Bandits Avishek Ghosh, Sayak Ray Chowdhury, Aditya Gopalan