Attias, Idan

22 publications

COLT 2025 Capacity-Constrained Online Learning with Delays: Scheduling Frameworks and Regret Trade-Offs Alexander Ryabchenko, Idan Attias, Daniel M. Roy

NeurIPS 2025 On Traceability in $\ell_p$ Stochastic Convex Optimization Sasha Voitovych, Mahdi Haghifam, Idan Attias, Gintare Karolina Dziugaite, Roi Livni, Daniel M. Roy

ICML 2025 PAC Learning with Improvements Idan Attias, Avrim Blum, Keziah Naggita, Donya Saless, Dravyansh Sharma, Matthew Walter

ALT 2025 Sample Compression Scheme Reductions Idan Attias, Steve Hanneke, Arvind Ramaswami

NeurIPS 2025 Tradeoffs Between Mistakes and ERM Oracle Calls in Online and Transductive Online Learning Idan Attias, Steve Hanneke, Arvind Ramaswami

ICML 2024 Agnostic Sample Compression Schemes for Regression Idan Attias, Steve Hanneke, Aryeh Kontorovich, Menachem Sadigurschi

ICML 2024 Causal Bandits: The Pareto Optimal Frontier of Adaptivity, a Reduction to Linear Bandits, and Limitations Around Unknown Marginals Ziyi Liu, Idan Attias, Daniel M. Roy

ICMLW 2024 Causal Bandits: The Pareto Optimal Frontier of Adaptivity, a Reduction to Linear Bandits, and Limitations Around Unknown Marginals Ziyi Liu, Idan Attias, Daniel M. Roy

JMLR 2024 Fat-Shattering Dimension of K-Fold Aggregations Idan Attias, Aryeh Kontorovich

ICML 2024 Information Complexity of Stochastic Convex Optimization: Applications to Generalization, Memorization, and Tracing Idan Attias, Gintare Karolina Dziugaite, Mahdi Haghifam, Roi Livni, Daniel M. Roy

NeurIPS 2024 Sequential Probability Assignment with Contexts: Minimax Regret, Contextual Shtarkov Sums, and Contextual Normalized Maximum Likelihood Ziyi Liu, Idan Attias, Daniel M. Roy

ICMLW 2024 The Minimax Regret of Sequential Probability Assignment, Contextual Shtarkov Sums, and Contextual Normalized Maximum Likelihood Ziyi Liu, Idan Attias, Daniel M. Roy

COLT 2024 Universal Rates for Regression: Separations Between Cut-Off and Absolute Loss Idan Attias, Steve Hanneke, Alkis Kalavasis, Amin Karbasi, Grigoris Velegkas

ICML 2023 Adversarially Robust PAC Learnability of Real-Valued Functions Idan Attias, Steve Hanneke

AAAI 2023 Learning Revenue Maximization Using Posted Prices for Stochastic Strategic Patient Buyers Eitan-Hai Mashiah, Idan Attias, Yishay Mansour

COLT 2023 Online Learning and Solving Infinite Games with an ERM Oracle Angelos Assos, Idan Attias, Yuval Dagan, Constantinos Daskalakis, Maxwell K. Fishelson

NeurIPS 2023 Optimal Learners for Realizable Regression: PAC Learning and Online Learning Idan Attias, Steve Hanneke, Alkis Kalavasis, Amin Karbasi, Grigoris Velegkas

NeurIPS 2022 A Characterization of Semi-Supervised Adversarially Robust PAC Learnability Idan Attias, Steve Hanneke, Yishay Mansour

TMLR 2022 Domain Invariant Adversarial Learning Matan Levi, Idan Attias, Aryeh Kontorovich

JMLR 2022 Improved Generalization Bounds for Adversarially Robust Learning Idan Attias, Aryeh Kontorovich, Yishay Mansour

NeurIPS 2020 Prediction with Corrupted Expert Advice Idan Amir, Idan Attias, Tomer Koren, Yishay Mansour, Roi Livni

ALT 2019 Improved Generalization Bounds for Robust Learning Idan Attias, Aryeh Kontorovich, Yishay Mansour