Gaurav Mahajan

According to our database, Gaurav Mahajan authored at least 18 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Realizable Learning is All You Need.

Max Hopkins, Daniel M. Kane, Shachar Lovett, Gaurav Mahajan

TheoretiCS, 2024

Improved classical shadows from local symmetries in the Schur basis.

Daniel Grier, Sihan Liu, Gaurav Mahajan

CoRR, 2024

Deciphering EEG Waves for the Generation of Images.

Gaurav Mahajan, Jeevan R, Divija L, P. Deekshitha Kumari, Surabhi Narayan

Proceedings of the 12th International Winter Conference on Brain-Computer Interface, 2024

2023

Computational and Statistical Complexity of Learning in Sequential Models

Gaurav Mahajan

PhD thesis, 2023

Do PAC-Learners Learn the Marginal Distribution?

Max Hopkins, Daniel M. Kane, Shachar Lovett, Gaurav Mahajan

CoRR, 2023

Learning Hidden Markov Models Using Conditional Samples.

Gaurav Mahajan, Sham M. Kakade, Akshay Krishnamurthy, Cyril Zhang

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

Exponential Hardness of Reinforcement Learning with Linear Function Approximation.

Sihan Liu, Gaurav Mahajan, Daniel Kane, Shachar Lovett, Gellért Weisz, Csaba Szepesvári

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022

Computational-Statistical Gaps in Reinforcement Learning.

Daniel Kane, Sihan Liu, Shachar Lovett, Gaurav Mahajan

CoRR, 2022

Computational-Statistical Gap in Reinforcement Learning.

Daniel Kane, Sihan Liu, Shachar Lovett, Gaurav Mahajan

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

Learning what to remember.

Robi Bhattacharjee, Gaurav Mahajan

Proceedings of the International Conference on Algorithmic Learning Theory, 29 March, 2022

Convergence of online k-means.

Geelon So, Gaurav Mahajan, Sanjoy Dasgupta

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

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

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, Ruosong Wang

Proceedings of the 38th International Conference on Machine Learning, 2021

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

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

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Point Location and Active Learning: Learning Halfspaces Almost Optimally.

Max Hopkins, Daniel Kane, Shachar Lovett, Gaurav Mahajan

Proceedings of the 61st IEEE Annual Symposium on Foundations of Computer Science, 2020

Noise-tolerant, Reliable Active Classification with Comparison Queries.

Max Hopkins, Daniel Kane, Shachar Lovett, Gaurav Mahajan

Proceedings of the Conference on Learning Theory, 2020

Optimality and Approximation with Policy Gradient Methods in Markov Decision Processes.

Alekh Agarwal, Sham M. Kakade, Jason D. Lee, Gaurav Mahajan

Proceedings of the Conference on Learning Theory, 2020