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Publications

Journal Papers

  1. A Simple Finite-Time Analysis of TD Learning with Linear Function Approximation
    Aritra Mitra
    IEEE Transactions on Automatic Control (TAC), 2024. [arXiv] [Link]
  2. Federated Temporal Difference Learning with Linear Function Approximation under Environmental Heterogeneity [arXiv]
    Han Wang, Aritra Mitra, Hamed Hassani, George J. Pappas, and James Anderson
    Transactions on Machine Learning Research (TMLR), 2024. [Link]
  3. Temporal Difference Learning with Compressed Updates: Error-Feedback Meets Reinforcement Learning
    Aritra Mitra, George J. Pappas, and Hamed Hassani
    Transactions on Machine Learning Research (TMLR), 2024. [Link]
  4. A Survey of Graph-Theoretic Approaches for Analyzing the Resilience of Networked Control Systems 
    Mohammad Pirani, Aritra Mitra, and Shreyas Sundaram
    Automatica, 2023. [arXiv]
  5. Federated TD Learning over Finite-Rate Erasure Channels: Linear Speedup under Markovian Sampling
    Nicolo Dal Fabbro, Aritra Mitra and George Pappas
    IEEE Control Systems Letters, 2023. [arXiv]
  6. Distributed State Estimation over Time-Varying Graphs: Exploiting the Age-of-Information
    Aritra Mitra, John A. Richards, Saurabh Bagchi and Shreyas Sundaram
    IEEE Transactions on Automatic Control, 2022. [arXiv] [ieeeexplore]
  7. On the Computational Complexity of the Secure State-Reconstruction Problem
    Yanwen Mao, Aritra Mitra, Shreyas Sundaram and Paulo Tabuada
    Automatica, 2022. [arXiv] [link]
  8. Distributed Inference with Sparse and Quantized Communication
    Aritra Mitra, John A. Richards, Saurabh Bagchi and Shreyas Sundaram
    IEEE Transactions on Signal Processing, 2021. [arXiv] [ ieeeexplore ]
  9. On the Impacts of Redundancy, Diversity, and Trust in Resilient Distributed State Estimation
    Aritra Mitra, Faiq Ghawash, Shreyas Sundaram and Waseem Abbas
    IEEE Transactions on Control of Network Systems, 2020. [ieeeexplore]
  10. A New Approach to Distributed Hypothesis Testing and Non-Bayesian Learning: Improved Learning Rate and Byzantine-Resilience
    Aritra Mitra, John A. Richards and Shreyas Sundaram
    IEEE Transactions on Automatic Control, 2020. [arXiv] [ ieeeexplore ]
  11. Byzantine-Resilient Distributed Observers for LTI Systems
    Aritra Mitra and Shreyas Sundaram
    Automatica, 2019. [arXiv] [ link ]
  12. Resilient Distributed State Estimation with Mobile Agents: Overcoming Byzantine Adversaries, Communication Losses, and Intermittent Measurements
    Aritra Mitra, John A. Richards, Saurabh Bagchi and Shreyas Sundaram
    Autonomous RobotsSpecial Issue on Foundations of Resilience for Networked Robotic Systems, 2019. [link]
  13. Distributed Observers for LTI Systems
    Aritra Mitra and Shreyas Sundaram
    IEEE Transactions on Automatic Control, 2018. [pdf] [ ieeeexplore ]

Peer-Reviewed Conference Papers

  1. Robust Q-Learning under Corrupted Rewards
    Sreejeet Maity and Aritra Mitra
    63rd IEEE Decision and Control Conference (CDC), 2024, Milan, Italy
  2. Towards Fast Rates for Federated and Multi-Task Reinforcement Learning
    Feng Zhu, Robert W. Heath, and Aritra Mitra
    63rd IEEE Decision and Control Conference (CDC), 2024, Milan, Italy
  3. DASA: Delay-Adaptive Multi-Agent Stochastic Approximation
    Nicolò Dal Fabbro*, Arman Adibi*, H. Vincent Poor, Sanjeev R. Kulkarni, Aritra Mitra, and George J. Pappas
    63rd IEEE Decision and Control Conference (CDC), 2024, Milan, Italy
  4. On the Convergence of Policy Gradient for Designing a Linear Quadratic Regulator by Leveraging a Proxy System
    Lintao Ye, Aritra Mitra, and Vijay Gupta
    63rd IEEE Decision and Control Conference (CDC), 2024, Milan, Italy
  5. Towards Model-Free LQR Control over Rate-Limited Channels
    Aritra Mitra*, Lintao Ye*, and Vijay Gupta
    6th Annual Learning for Dynamics & Control Conference (L4DC), 2024, Oxford, UK. [arXiv]
    (Selected for Oral Presentation, Top 7.5%)
  6. Stochastic Approximation with Delayed Updates: Finite-Time Rates under Markovian Sampling
    Arman Adibi*, Nicolò Dal Fabbro*, Luca Schenato, Sanjeev Kulkarni, H. Vincent Poor, George J. Pappas, Hamed Hassani, and Aritra Mitra
    27th International Conference on Artificial Intelligence and Statistics (AISTATS), 2024, Valencia, Spain. [arXiv]
  7. Finite-Time Analysis of On-Policy Heterogeneous Federated Reinforcement Learning
    Chenyu Zhang, Han Wang, Aritra Mitra, and James Anderson
    12th International Conference on Learning Representations (ICLR), 2024, Vienna, Austria. [Link]
  8. Min-Max Optimization under Delays
    Arman Adibi, Aritra Mitra, and Hamed Hassani
    American Control Conference (ACC), 2024, Toronto, Canada. [arXiv]
  9. Finite-Time Analysis of Asynchronous Multi-Agent TD Learning
    Nicolò Dal Fabbro, Arman Adibi, Aritra Mitra, and George J. Pappas
    American Control Conference (ACC), 2024, Toronto, Canada.
  10. Linear Stochastic Bandits over a Bit-Constrained Channel
    Aritra Mitra, Hamed Hassani, and George J. Pappas
    5th Annual Learning for Dynamics and Control Conference (L4DC), 2023,
    Philadelphia, USA. [arXiv]
    (One of 16/167 papers selected for Oral Presentation)
  11. Collaborative Linear Bandits with Adversarial Agents: Near-Optimal Regret Bounds
    Aritra Mitra*, Arman Adibi*, George J. Pappas, and Hamed Hassani
    Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS), 2022. [arXiv]
  12. Distributed Statistical Min-Max Learning in the Presence of Byzantine Agents
    Arman Adibi*, Aritra Mitra*, George J. Pappas, and Hamed Hassani
    61st IEEE Decision and Control Conference (CDC), 2022, Cancun, Mexico. [arXiv]
  13. Distributed Hypothesis Testing and Social Learning in Finite Time with a Finite Amount of Communication
    Shreyas Sundaram and Aritra Mitra
    61st IEEE Decision and Control Conference (CDC), 2022, Cancun, Mexico.
  14. Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients
    Aritra Mitra, Rayana Jaafar, George J. Pappas and Hamed Hassani
    Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS), 2021. [arXiv] [Link] [Poster] [Talk at Google FL Workshop]
  15. Online Federated Learning
    Aritra Mitra, Hamed Hassani, and George J. Pappas
    60th IEEE Decision and Control Conference (CDC), 2021, Austin, Texas, USA.
  16. Federated Learning with Incrementally Aggregated Gradients
    Aritra Mitra*, Rayana Jaafar*, George J. Pappas and Hamed Hassani
    60th IEEE Decision and Control Conference (CDC), 2021, Austin, Texas, USA.
  17. Near-Optimal Data Source Selection for Bayesian Learning
    Lintao Ye, Aritra Mitra and Shreyas Sundaram
    3rd Annual Learning for Dynamics and Control Conference (L4DC), 2021, ETH Zurich, Switzerland. [arXiv]
  18. Event-Triggered Distributed Inference
    Aritra Mitra, Saurabh Bagchi and Shreyas Sundaram
    59th IEEE Decision and Control Conference (CDC), 2020, Jeju Island, Republic of Korea.
  19. A Communication-Efficient Algorithm for Exponentially Fast Non-Bayesian Learning in Networks
    Aritra Mitra, John A. Richards and Shreyas Sundaram
    58th IEEE Decision and Control Conference (CDC), 2019, Nice, France. [arXiv]
  20. When is the Secure State-Reconstruction Problem Hard?
    Yanwen Mao, Aritra Mitra, Shreyas Sundaram and Paulo Tabuada
    58th IEEE Decision and Control Conference (CDC), 2019, Nice, France.
  21. A New Approach for Distributed Hypothesis Testing with Extensions to Byzantine-Resilience
    Aritra Mitra, John A. Richards and Shreyas Sundaram
    American Control Conference (ACC), 2019, Philadelphia, USA. [arXiv]
  22. Finite-Time Distributed State Estimation over Time-Varying Graphs: Exploiting the Age-of-Information
    Aritra Mitra, John A. Richards, Saurabh Bagchi and Shreyas Sundaram
    American Control Conference (ACC), 2019, Philadelphia, USA. [arXiv]
  23. A Novel Switched Linear Observer for Estimating the State of a Dynamical Process with a Mobile Agent
    Aritra Mitra and Shreyas Sundaram
    57th IEEE Decision and Control Conference (CDC), 2018, Miami, USA. [pdf]
  24. On the Impact of Trusted Nodes in Resilient Distributed State Estimation of LTI Systems
    Aritra Mitra, Waseem Abbas and Shreyas Sundaram
    57th IEEE Decision and Control Conference (CDC), 2018, Miami, USA.
  25. Secure Distributed State Estimation of an LTI System over Time-Varying Networks and Analog Erasure Channels
    Aritra Mitra and Shreyas Sundaram
    American Control Conference (ACC), 2018, Milwaukee, USA. [pdf]
  26. Distributed Functional Observers for LTI Systems
    Aritra Mitra and Shreyas Sundaram
    56th IEEE Decision and Control Conference (CDC), 2017, Melbourne, Australia. [pdf]
  27. Secure Distributed Observers for a Class of Linear Time Invariant Systems in the presence of Byzantine Adversaries
    Aritra Mitra and Shreyas Sundaram
    55th IEEE Decision and Control Conference (CDC), 2016, Las Vegas, NV, USA. [pdf]
  28. An Approach for Distributed State Estimation of LTI Systems
    Aritra Mitra and Shreyas Sundaram
    54th Annual Allerton Conference, 2016, IL, USA. [pdf]
  29. Control of a 4 DOF Barrett WAM Robot: Modeling, Control Synthesis and Experimental Validation
    Aritra Mitra, Niladri Das, Raj Nayan Samant, and Laxmidhar Behera
    1st IEEE International Conferance on Control, Measurement and Instrumentation (CMI), 2016, Kolkata, India.
  30. Continuous-Time Single Network Adaptive Critic based Optimal Sliding Mode Control for Nonlinear Control Affine Systems
    Aritra Mitra and Laxmidhar Behera
    34th Chinese Control Conference (CCC), 2015, Hangzhou, China.
  31. Development of a Fuzzy Sliding Mode Controller with Adaptive Tuning Technique for a MRI Guided Robot in the Human Vasculature
    Aritra Mitra and Laxmidhar Behera
    13th IEEE International Conference on Industrial Informatics (INDIN), 2015, Cambridge, UK.

* Denotes equal contribution

Theses

  • New Approaches to Distributed State Estimation, Inference and Learning with Extensions to Byzantine-Resilience , Ph.D. Thesis, Purdue University, 2020. [pdf]
  • Sliding Mode Control Strategies for Robotic Systems , M.tech Thesis, IIT Kanpur, 2015. [pdf]