Gaurav Mahajan

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 27

Top Topics

Markov Decision Process

Function Approximation

Reinforcement Learning

Top Venues

Publications

Max Hopkins, Daniel M. Kane, Shachar Lovett, Gaurav Mahajan
Realizable Learning is All You Need. TheoretiCS 3 (2024)
Gaurav Mahajan, Jeevan R, Divija L, P. Deekshitha Kumari, Surabhi Narayan
Deciphering EEG Waves for the Generation of Images. BCI (2024)
Daniel Grier, Sihan Liu, Gaurav Mahajan
Improved classical shadows from local symmetries in the Schur basis. CoRR (2024)
Max Hopkins, Daniel M. Kane, Shachar Lovett, Gaurav Mahajan
Do PAC-Learners Learn the Marginal Distribution? CoRR (2023)
Gaurav Mahajan, Sham M. Kakade, Akshay Krishnamurthy, Cyril Zhang
Learning Hidden Markov Models Using Conditional Samples. COLT (2023)
Sham M. Kakade, Akshay Krishnamurthy, Gaurav Mahajan, Cyril Zhang
Learning Hidden Markov Models Using Conditional Samples. CoRR (2023)
Daniel Kane, Sihan Liu, Shachar Lovett, Gaurav Mahajan, Csaba Szepesvári, Gellért Weisz
Exponential Hardness of Reinforcement Learning with Linear Function Approximation. CoRR (2023)
Sihan Liu, Gaurav Mahajan, Daniel Kane, Shachar Lovett, Gellért Weisz, Csaba Szepesvári
Exponential Hardness of Reinforcement Learning with Linear Function Approximation. COLT (2023)
Daniel Kane, Sihan Liu, Shachar Lovett, Gaurav Mahajan
Computational-Statistical Gap in Reinforcement Learning. COLT (2022)
Daniel Kane, Sihan Liu, Shachar Lovett, Gaurav Mahajan
Computational-Statistical Gaps in Reinforcement Learning. CoRR (2022)
Robi Bhattacharjee, Gaurav Mahajan
Learning what to remember. CoRR (2022)
Sanjoy Dasgupta, Gaurav Mahajan, Geelon So
Convergence of online k-means. CoRR (2022)
Robi Bhattacharjee, Gaurav Mahajan
Learning what to remember. ALT (2022)
Max Hopkins, Daniel M. Kane, Shachar Lovett, Gaurav Mahajan
Realizable Learning is All You Need. COLT (2022)
Geelon So, Gaurav Mahajan, Sanjoy Dasgupta
Convergence of online k-means. AISTATS (2022)
Max Hopkins, Daniel Kane, Shachar Lovett, Gaurav Mahajan
Realizable Learning is All You Need. CoRR (2021)
Alekh Agarwal, Sham M. Kakade, Jason D. Lee, Gaurav Mahajan
On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shift. J. Mach. Learn. Res. 22 (2021)
Simon S. Du, Sham M. Kakade, Jason D. Lee, Shachar Lovett, Gaurav Mahajan, Wen Sun, Ruosong Wang
Bilinear Classes: A Structural Framework for Provable Generalization in RL. ICML (2021)
Simon S. Du, Sham M. Kakade, Jason D. Lee, Shachar Lovett, Gaurav Mahajan, Wen Sun, Ruosong Wang
Bilinear Classes: A Structural Framework for Provable Generalization in RL. CoRR (2021)
Max Hopkins, Daniel Kane, Shachar Lovett, Gaurav Mahajan
Point Location and Active Learning: Learning Halfspaces Almost Optimally. FOCS (2020)
Simon S. Du, Jason D. Lee, Gaurav Mahajan, Ruosong Wang
Agnostic $Q$-learning with Function Approximation in Deterministic Systems: Near-Optimal Bounds on Approximation Error and Sample Complexity. NeurIPS (2020)
Max Hopkins, Daniel M. Kane, Shachar Lovett, Gaurav Mahajan
Point Location and Active Learning: Learning Halfspaces Almost Optimally. CoRR (2020)
Alekh Agarwal, Sham M. Kakade, Jason D. Lee, Gaurav Mahajan
Optimality and Approximation with Policy Gradient Methods in Markov Decision Processes. COLT (2020)
Simon S. Du, Jason D. Lee, Gaurav Mahajan, Ruosong Wang
Agnostic Q-learning with Function Approximation in Deterministic Systems: Tight Bounds on Approximation Error and Sample Complexity. CoRR (2020)
Max Hopkins, Daniel Kane, Shachar Lovett, Gaurav Mahajan
Noise-tolerant, Reliable Active Classification with Comparison Queries. COLT (2020)
Max Hopkins, Daniel Kane, Shachar Lovett, Gaurav Mahajan
Noise-tolerant, Reliable Active Classification with Comparison Queries. CoRR (2020)
Alekh Agarwal, Sham M. Kakade, Jason D. Lee, Gaurav Mahajan
Optimality and Approximation with Policy Gradient Methods in Markov Decision Processes. CoRR (2019)