Imon Banerjee

Imon Banerjee

Northwestern University: IEMS Alumni Fellow, Purdue University: PhD, Indian Statistical Institute: B.S. & M.S.

My current and ongoing projects

Work in Progress

  1. Banerjee, B., Banerjee, I., & Das, S. (2026+). Central Limit Theorem for the Intrinsic Fréchet Means of Random Samples from Stiefel Manifolds.
  2. Banerjee, I., & Taşkesen, B. (2026+). Variational Inference and Bayesian Sampling under Knothe–Rosenblatt Dependence.
  3. Banerjee, I., & Alquier, P. (2026). Empirical PAC-Bayesian Bounds and Posterior Concentration for Regenerative Processes.
  4. Lei, J., Banerjee, I., & Mehrotra, S. (2026). Mixed-Integer Optimization for Nonparametric Changepoint Detection with Epidemic Regimes.

Working Papers

  1. Banerjee, I., & Bhattacharyya, R. (2026). Low Dimensional Sampling under Reconstructed Constraints. Under review at the NeurIPS MLxOR Workshop for the MathOR journal track. Preprint
  2. Kasturi, T., Salzman, K. A., Anandganesh, B., Huynh, D., Banerjee, I., & Mukhopadhyay, S. (2026). NKOOD: Nonparametric Kernel-Attention Out-of-Distribution Detection with Finite-Sample Guarantees. Preprint
  3. Su, Z., Banerjee, I., & Klabjan, D. (2026). Central Limit Theorem for the Transition Matrices of Controlled Markov Chains. Under review. Preprint
  4. Banerjee, I., & Gurvich, I. (2026). Goggin’s Corrected Kalman Filter: Guarantees and Filtering Regimes. Second revision at IEEE Transactions on Information Theory. Preprint
  5. Banerjee, I., Honnappa, H., & Rao, V. A. (2026). Adaptive Estimation of the Transition Densities of Controlled Markov Chains. Preprint

Published / Accepted Work

  1. Su, Z., Banerjee, I., & Klabjan, D. (2026). Model-Based Bootstrap of Controlled Markov Chains. Accepted at NeurIPS. Preprint
  2. Banerjee, I., Chakrabarty, S., Samanta, R., & Bhattacharyya, R. (2026). The Type Theory of Stationary MDPs: Rare Events and Uncertainty Quantification. Accepted at NeurIPS.
  3. Bhattacharyya, R., Chakrabarty, S., & Banerjee, I. (2026). Adaptive Model Selection in Offline Contextual MDPs without Stationarity. Transactions of Machine Learning Research. link
  4. Banerjee, I., & Honorio, J. (2026). Meta Sparse Principal Component Analysis. AISTATS. link
  5. Banerjee, I., Lei, J., & Mehrotra, S. (2026). Nonparametric Multi Change Point Detection for Markov Chains via Adaptive Clustering. AISTATS. link
  6. Banerjee, I., & Chakrabarty, S. (2025). CLT and Edgeworth Expansion for m-out-of-n Bootstrap Estimators of the Studentized Median. NeurIPS. link
  7. Banerjee, I., Honnappa, H., & Rao, V. A. (2025). Offline Estimation of Controlled Markov Chains: Minimaxity and Sample Complexity. Operations Research. link
  8. Banerjee, I., Rao, V. A., & Honnappa, H. (2021). PAC-Bayes Bounds on Variational Tempered Posteriors for Markov Models. Entropy. link
  9. Banerjee, I., Mullick, S. S., & Das, S. (2019). On Convergence of the Class Membership Estimator in Fuzzy k-Nearest Neighbor Classifier. IEEE Transactions on Fuzzy Systems. link