Lovett, Shachar

11 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 2022 Computational-Statistical Gap in Reinforcement Learning Daniel Kane, Sihan Liu, Shachar Lovett, 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

COLT 2021 Bounded Memory Active Learning Through Enriched Queries Max Hopkins, Daniel Kane, Shachar Lovett, Michal Moshkovitz

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

NeurIPS 2020 The Power of Comparisons for Actively Learning Linear Classifiers Max Hopkins, Daniel Kane, Shachar Lovett

NeurIPS 2020 Towards a Combinatorial Characterization of Bounded-Memory Learning Alon Gonen, Shachar Lovett, Michal Moshkovitz

COLT 2017 Noisy Population Recovery from Unknown Noise Shachar Lovett, Jiapeng Zhang

COLT 2012 Unsupervised SVMs: On the Complexity of the Furthest Hyperplane Problem Zohar Karnin, Edo Liberty, Shachar Lovett, Roy Schwartz, Omri Weinstein