Mitra, Aritra

13 publications

TMLR 2026 Achieving Tighter Finite-Time Rates for Heterogeneous Federated Stochastic Approximation Under Markovian Sampling Feng Zhu, Aritra Mitra, Robert W. Heath

TMLR 2026 Model-Free Learning with Heterogeneous Dynamical Systems: A Federated LQR Approach Han Wang, Leonardo Felipe Toso, Aritra Mitra, James Anderson

AISTATS 2025 Adversarially-Robust TD Learning with Markovian Data: Finite-Time Rates and Fundamental Limits Sreejeet Maity, Aritra Mitra

L4DC 2025 Outlier-Robust Linear System Identification Under Heavy-Tailed Noise Vinay Kanakeri, Aritra Mitra

TMLR 2024 Federated TD Learning with Linear Function Approximation Under Environmental Heterogeneity Han Wang, Aritra Mitra, Hamed Hassani, George J. Pappas, James Anderson

ICLR 2024 Finite-Time Analysis of On-Policy Heterogeneous Federated Reinforcement Learning Chenyu Zhang, Han Wang, Aritra Mitra, James Anderson

AISTATS 2024 Stochastic Approximation with Delayed Updates: Finite-Time Rates Under Markovian Sampling Arman Adibi, Nicolò Fabbro, Luca Schenato, Sanjeev Kulkarni, H. Vincent Poor, George J. Pappas, Hamed Hassani, Aritra Mitra

TMLR 2024 Temporal Difference Learning with Compressed Updates: Error-Feedback Meets Reinforcement Learning Aritra Mitra, George J. Pappas, Hamed Hassani

L4DC 2024 Towards Model-Free LQR Control over Rate-Limited Channels Aritra Mitra, Lintao Ye, Vijay Gupta

L4DC 2023 Linear Stochastic Bandits over a Bit-Constrained Channel Aritra Mitra, Hamed Hassani, George J. Pappas

NeurIPS 2022 Collaborative Linear Bandits with Adversarial Agents: Near-Optimal Regret Bounds Aritra Mitra, Arman Adibi, George J. Pappas, Hamed Hassani

NeurIPS 2021 Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients Aritra Mitra, Rayana Jaafar, George J. Pappas, Hamed Hassani

L4DC 2021 Near-Optimal Data Source Selection for Bayesian Learning Lintao Ye, Aritra Mitra, Shreyas Sundaram