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ORCIDGaurav Mahajan
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2020 – today
- 2024
[j2]Max Hopkins, Daniel M. Kane, Shachar Lovett, Gaurav Mahajan:
Realizable Learning is All You Need. TheoretiCS 3 (2024)- [c12]Gaurav Mahajan, Jeevan R, Divija L, P. Deekshitha Kumari, Surabhi Narayan:
Deciphering EEG Waves for the Generation of Images. BCI 2024: 1-6 - [i13]Daniel Grier, Sihan Liu, Gaurav Mahajan:
Improved classical shadows from local symmetries in the Schur basis. CoRR abs/2405.09525 (2024) - 2023
- [b1]Gaurav Mahajan:
Computational and Statistical Complexity of Learning in Sequential Models. University of California, San Diego, USA, 2023 - [c11]Sihan Liu, Gaurav Mahajan, Daniel Kane, Shachar Lovett, Gellért Weisz, Csaba Szepesvári:
Exponential Hardness of Reinforcement Learning with Linear Function Approximation. COLT 2023: 1588-1617 - [c10]Gaurav Mahajan, Sham M. Kakade, Akshay Krishnamurthy, Cyril Zhang:
Learning Hidden Markov Models Using Conditional Samples. COLT 2023: 2014-2066 - [i12]Max Hopkins, Daniel M. Kane, Shachar Lovett, Gaurav Mahajan:
Do PAC-Learners Learn the Marginal Distribution? CoRR abs/2302.06285 (2023) - [i11]Daniel Kane, Sihan Liu, Shachar Lovett, Gaurav Mahajan, Csaba Szepesvári, Gellért Weisz:
Exponential Hardness of Reinforcement Learning with Linear Function Approximation. CoRR abs/2302.12940 (2023) - [i10]Sham M. Kakade, Akshay Krishnamurthy, Gaurav Mahajan, Cyril Zhang:
Learning Hidden Markov Models Using Conditional Samples. CoRR abs/2302.14753 (2023) - 2022
- [c9]Geelon So, Gaurav Mahajan, Sanjoy Dasgupta:
Convergence of online k-means. AISTATS 2022: 8534-8569 - [c8]Robi Bhattacharjee, Gaurav Mahajan:
Learning what to remember. ALT 2022: 70-89 - [c7]Daniel Kane, Sihan Liu, Shachar Lovett, Gaurav Mahajan:
Computational-Statistical Gap in Reinforcement Learning. COLT 2022: 1282-1302 - [c6]Max Hopkins, Daniel M. Kane, Shachar Lovett, Gaurav Mahajan:
Realizable Learning is All You Need. COLT 2022: 3015-3069 - [i9]Robi Bhattacharjee, Gaurav Mahajan:
Learning what to remember. CoRR abs/2201.03806 (2022) - [i8]Daniel Kane, Sihan Liu, Shachar Lovett, Gaurav Mahajan:
Computational-Statistical Gaps in Reinforcement Learning. CoRR abs/2202.05444 (2022) - [i7]Sanjoy Dasgupta, Gaurav Mahajan, Geelon So:
Convergence of online k-means. CoRR abs/2202.10640 (2022) - 2021
- [j1]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: 98:1-98:76 (2021) - [c5]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: 2826-2836 - [i6]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 abs/2103.10897 (2021) - [i5]Max Hopkins, Daniel M. Kane, Shachar Lovett, Gaurav Mahajan:
Realizable Learning is All You Need. CoRR abs/2111.04746 (2021) - 2020
- [c4]Alekh Agarwal, Sham M. Kakade, Jason D. Lee, Gaurav Mahajan:
Optimality and Approximation with Policy Gradient Methods in Markov Decision Processes. COLT 2020: 64-66 - [c3]Max Hopkins, Daniel Kane, Shachar Lovett, Gaurav Mahajan:
Noise-tolerant, Reliable Active Classification with Comparison Queries. COLT 2020: 1957-2006 - [c2]Max Hopkins, Daniel Kane, Shachar Lovett, Gaurav Mahajan:
Point Location and Active Learning: Learning Halfspaces Almost Optimally. FOCS 2020: 1034-1044 - [c1]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 - [i4]Max Hopkins, Daniel Kane, Shachar Lovett, Gaurav Mahajan:
Noise-tolerant, Reliable Active Classification with Comparison Queries. CoRR abs/2001.05497 (2020) - [i3]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 abs/2002.07125 (2020) - [i2]Max Hopkins, Daniel M. Kane, Shachar Lovett, Gaurav Mahajan:
Point Location and Active Learning: Learning Halfspaces Almost Optimally. CoRR abs/2004.11380 (2020)
2010 – 2019
- 2019
- [i1]Alekh Agarwal, Sham M. Kakade, Jason D. Lee, Gaurav Mahajan:
Optimality and Approximation with Policy Gradient Methods in Markov Decision Processes. CoRR abs/1908.00261 (2019)
Coauthor Index
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