My research interests broadly lie in the areas of reinforcement learning and online learning. Currently, my research focuses on designing low-complexity learning algorithms for large systems, using ideas from probability theory, optimization, and information theory.

Provably Adaptive Average Reward Reinforcement Learning for Metric Spaces. Authors: Avik Kar and Rahul Singh. arXiv preprint arXiv:2410.19919

Fantom: Federated Adversarial Network for Training Multi-Sequence Magnetic Resonance Imaging in Semantic Segmentation. Authors: Anupam Borthakur, Apoorva Srivastava, Avik Kar, Dipayan Dewan, and Debdoot Sheet. International Conference on Image Processing (ICIP). IEEE, 2024.

Linear Bandits With Side Observations on Networks. Authors: Avik Kar, Rahul Singh, Fang Liu, Xin Liu, and Ness B. Shroff. IEEE/ACM Transactions on Networking, 2024.

Policy Zooming: Adaptive Discretization-based Infinite-Horizon Average-Reward Reinforcement Learning. Authors: Avik Kar and Rahul Singh. arXiv preprint arXiv:2405.18793

Finite Time Logarithmic Regret Bounds for Self-Tuning Regulation. Authors: Rahul Singh, Akshay Mete, Avik Kar, and P R Kumar. 41st International Conference on Machine Learning (ICML), 2024.

Federated Learning for Site Aware Chest Radiograph Screening. Authors: Arunava Chakravarty, Avik Kar, Ramanathan Sethuraman, and Debdoot Sheet. 18th International Symposium on Biomedical Imaging (ISBI). IEEE, 2021.