Optimality and Approximation with Policy Gradient Methods in Markov Decision Processes. - Alekh Agarwal; Sham M. Kakade; Jason D. Lee; Gaurav Mahajan (COLT, 2020)

Noise-tolerant, Reliable Active Classification with Comparison Queries. - Max Hopkins; Daniel Kane 001; Shachar Lovett; Gaurav Mahajan (COLT, 2020)

Point Location and Active Learning: Learning Halfspaces Almost Optimally. - Max Hopkins; Daniel Kane 001; Shachar Lovett; Gaurav Mahajan (FOCS, 2020)

Agnostic $Q$-learning with Function Approximation in Deterministic Systems: Near-Optimal Bounds on Approximation Error and Sample Complexity. - Simon S. Du; Jason D. Lee; Gaurav Mahajan; Ruosong Wang (NeurIPS, 2020)

Agnostic Q-learning with Function Approximation in Deterministic Systems: Tight Bounds on Approximation Error and Sample Complexity. - Simon S. Du; Jason D. Lee; Gaurav Mahajan; Ruosong Wang (CoRR, 2020)

Bilinear Classes: A Structural Framework for Provable Generalization in RL. - Simon S. Du; Sham M. Kakade; Jason D. Lee; Shachar Lovett; Gaurav Mahajan; Wen Sun 002; Ruosong Wang (ICML, 2021)

On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shift. - Alekh Agarwal; Sham M. Kakade; Jason D. Lee; Gaurav Mahajan (J. Mach. Learn. Res., 2021)

Convergence of online k-means. - Geelon So; Gaurav Mahajan; Sanjoy Dasgupta (AISTATS, 2022)

Learning what to remember. - Robi Bhattacharjee; Gaurav Mahajan (ALT, 2022)

Computational-Statistical Gap in Reinforcement Learning. - Daniel Kane 001; Sihan Liu; Shachar Lovett; Gaurav Mahajan (COLT, 2022)

Computational-Statistical Gaps in Reinforcement Learning. - Daniel Kane 001; Sihan Liu; Shachar Lovett; Gaurav Mahajan (CoRR, 2022)

Exponential Hardness of Reinforcement Learning with Linear Function Approximation. - Sihan Liu; Gaurav Mahajan; Daniel Kane 001; Shachar Lovett; Gellért Weisz; Csaba Szepesvári (COLT, 2023)

Learning Hidden Markov Models Using Conditional Samples. - Gaurav Mahajan; Sham M. Kakade; Akshay Krishnamurthy; Cyril Zhang (COLT, 2023)

Computational and Statistical Complexity of Learning in Sequential Models - Gaurav Mahajan (2023)

Deciphering EEG Waves for the Generation of Images. - Gaurav Mahajan; Jeevan R; Divija L; P. Deekshitha Kumari; Surabhi Narayan (BCI, 2024)

Improved classical shadows from local symmetries in the Schur basis. - Daniel Grier; Sihan Liu; Gaurav Mahajan (CoRR, 2024)

Realizable Learning is All You Need. - Max Hopkins; Daniel M. Kane; Shachar Lovett; Gaurav Mahajan (TheoretiCS, 2024)

Do PAC-Learners Learn the Marginal Distribution? - Max Hopkins; Daniel M. Kane; Shachar Lovett; Gaurav Mahajan (ALT, 2025)