My current and ongoing projects

Ongoing Work

1. Gupta, V., Gurvich, I., Banerjee, I. (2026+) Optimal Control Problem for Particle Filters with Non-Gaussian Noise.
2. Lei, J., Banerjee, I., & Mehrotra, S. (2026+). A PCA formulation for Multi Change Point Detection on Regenerative Processes (Preprint on request).
3. So, T., Banerjee, I., & Klabjan, D. (2026). Model Based Bootstrapping for the Transition Probabilities of Controlled Markov Chains (Preprint on request).
4. So, T., Banerjee, I., & Klabjan, D. (2026+). Central Limit Theorems for Transition Probabilities of Controlled Markov Chains. Preprint
5. Banerjee, I., Honnappa, H., & Rao, V. A. (2026). Adaptive Estimation of the Transition Densities of Controlled Markov Chains. Preprint
6. Banerjee, I., Gurvich, I. (2026). Goggin’s Corrected Kalman Filter: Guarantees and Filtering Regimes. Revision Submitted at IEEE Tran-IT Preprint

Published Work

1. Bhattacharyya, R., Chakraborty, S., Banerjee, I.. Adaptive Model Selection in Offline Contextual MDP’s without Stationarity. Accepted at ACM Transactions of Machine Learning Research, 2026
2. Banerjee, I., & Honorio, J. Meta Sparse Principal Component Analysis. AISTATS, 2026. link
3. Banerjee, I., Lei, J., & Mehrotra, S. Nonparametric Multi Change Point Detection for Markov Chains via Adaptive Clustering. AISTATS, 2026 link.
4. Banerjee, I., Chakraborty, S. CLT and Edgeworth Expansion for m-out-of-n Bootstrap Estimators of The Studentized Median. NeurIPS, 2025. link
5. Banerjee, I., Honnappa, H., & Rao, V. A.. Offline Estimation of Controlled Markov Chains: Minimaxity and Sample Complexity. Operations Research, 2025. link
6. Banerjee, I., Rao, V. A., & Honnappa, H.. PAC-Bayes Bounds on Variational Tempered Posteriors for Markov Models. Approximate Bayesian Inference, Entropy, 2021 link
7. Banerjee, I., Mullick, S. S., & Das, S.. On Convergence of the Class Membership Estimator in Fuzzy k-Nearest Neighbor Classifier. IEEE Transactions on Fuzzy Systems, 2019 link