Mahajan, Gaurav

12 publications

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

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

COLT 2023 Learning Hidden Markov Models Using Conditional Samples Gaurav Mahajan, Sham Kakade, Akshay Krishnamurthy, Cyril Zhang

AISTATS 2022 Convergence of Online K-Means Geelon So, Gaurav Mahajan, Sanjoy Dasgupta

COLT 2022 Computational-Statistical Gap in Reinforcement Learning Daniel Kane, Sihan Liu, Shachar Lovett, Gaurav Mahajan

ALT 2022 Learning What to Remember Robi Bhattacharjee, Gaurav Mahajan

COLT 2022 Realizable Learning Is All You Need Max Hopkins, Daniel M. Kane, Shachar Lovett, Gaurav Mahajan

ICML 2021 Bilinear Classes: A Structural Framework for Provable Generalization in RL Simon Du, Sham Kakade, Jason Lee, Shachar Lovett, Gaurav Mahajan, Wen Sun, Ruosong Wang

JMLR 2021 On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shift Alekh Agarwal, Sham M. Kakade, Jason D. Lee, Gaurav Mahajan

NeurIPS 2020 Agnostic $q$-Learning with Function Approximation in Deterministic Systems: Near-Optimal Bounds on Approximation Error and Sample Complexity Simon S Du, Jason Lee, Gaurav Mahajan, Ruosong Wang

COLT 2020 Noise-Tolerant, Reliable Active Classification with Comparison Queries Max Hopkins, Daniel Kane, Shachar Lovett, Gaurav Mahajan

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