A Peak Synchronization Measure for Multiple Signals. - Rahul Biswas; Koulik Khamaru; Kaushik Kumar Majumdar (IEEE Trans. Signal Process., 2014)

Theoretical guarantees for EM under misspecified Gaussian mixture models. - Raaz Dwivedi; Nhat Ho; Koulik Khamaru; Martin J. Wainwright; Michael I. Jordan (NeurIPS, 2018)

Challenges with EM in application to weakly identifiable mixture models. - Raaz Dwivedi; Nhat Ho; Koulik Khamaru; Martin J. Wainwright; Michael I. Jordan; Bin Yu 001 (CoRR, 2019)

Convergence Guarantees for a Class of Non-convex and Non-smooth Optimization Problems. - Koulik Khamaru; Martin J. Wainwright (J. Mach. Learn. Res., 2019)

Computation of the maximum likelihood estimator in low-rank factor analysis. - Koulik Khamaru; Rahul Mazumder (Math. Program., 2019)

Sharp Analysis of Expectation-Maximization for Weakly Identifiable Models. - Raaz Dwivedi; Nhat Ho; Koulik Khamaru; Martin J. Wainwright; Michael I. Jordan; Bin Yu 001 (AISTATS, 2020)

Instability, Computational Efficiency and Statistical Accuracy. - Nhat Ho; Koulik Khamaru; Raaz Dwivedi; Martin J. Wainwright; Michael I. Jordan; Bin Yu 001 (CoRR, 2020)

Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems. - Dhruv Malik; Ashwin Pananjady; Kush Bhatia; Koulik Khamaru; Peter L. Bartlett; Martin J. Wainwright (J. Mach. Learn. Res., 2020)

Instance-optimality in optimal value estimation: Adaptivity via variance-reduced Q-learning. - Koulik Khamaru; Eric Xia; Martin J. Wainwright; Michael I. Jordan (CoRR, 2021)

Near-optimal inference in adaptive linear regression. - Koulik Khamaru; Yash Deshpande; Lester Mackey; Martin J. Wainwright (CoRR, 2021)

Is Temporal Difference Learning Optimal? An Instance-Dependent Analysis. - Koulik Khamaru; Ashwin Pananjady; Feng Ruan; Martin J. Wainwright; Michael I. Jordan (SIAM J. Math. Data Sci., 2021)

Optimal variance-reduced stochastic approximation in Banach spaces. - Wenlong Mou; Koulik Khamaru; Martin J. Wainwright; Peter L. Bartlett; Michael I. Jordan (CoRR, 2022)

Statistical Limits of Adaptive Linear Models: Low-Dimensional Estimation and Inference. - Licong Lin; Mufang Ying; Suvrojit Ghosh; Koulik Khamaru; Cun-Hui Zhang (NeurIPS, 2023)

Adaptive Linear Estimating Equations. - Mufang Ying; Koulik Khamaru; Cun-Hui Zhang (NeurIPS, 2023)

Semi-parametric inference based on adaptively collected data. - Licong Lin; Koulik Khamaru; Martin J. Wainwright (CoRR, 2023)

Instance-Dependent Confidence and Early Stopping for Reinforcement Learning. - Eric Xia; Koulik Khamaru; Martin J. Wainwright; Michael I. Jordan (J. Mach. Learn. Res., 2023)

Stochastic Optimization with Constraints: A Non-asymptotic Instance-Dependent Analysis. - Koulik Khamaru (CoRR, 2024)

Inference with the Upper Confidence Bound Algorithm. - Koulik Khamaru; Cun-Hui Zhang (CoRR, 2024)

UCB algorithms for multi-armed bandits: Precise regret and adaptive inference. - Qiyang Han; Koulik Khamaru; Cun-Hui Zhang (CoRR, 2024)

Stable Thompson Sampling: Valid Inference via Variance Inflation. - Budhaditya Halder; Shubhayan Pan; Koulik Khamaru (CoRR, 2025)