Improved Generalization Bounds for Robust Learning. - Idan Attias; Aryeh Kontorovich; Yishay Mansour (ALT, 2019)

Prediction with Corrupted Expert Advice. - Idan Amir; Idan Attias; Tomer Koren; Yishay Mansour; Roi Livni (NeurIPS, 2020)

Fat-shattering dimension of k-fold maxima. - Aryeh Kontorovich; Idan Attias (CoRR, 2021)

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

Stochastic Strategic Patient Buyers: Revenue maximization using posted prices. - Eitan-Hai Mashiah; Idan Attias; Yishay Mansour (CoRR, 2022)

Adversarially Robust Learning of Real-Valued Functions. - Idan Attias; Steve Hanneke (CoRR, 2022)

Improved Generalization Bounds for Adversarially Robust Learning. - Idan Attias; Aryeh Kontorovich; Yishay Mansour (J. Mach. Learn. Res., 2022)

Domain Invariant Adversarial Learning. - Matan Levi; Idan Attias; Aryeh Kontorovich (Trans. Mach. Learn. Res., 2022)

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

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

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

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

Universal Rates for Regression: Separations between Cut-Off and Absolute Loss. - Idan Attias; Steve Hanneke; Alkis Kalavasis; Amin Karbasi; Grigoris Velegkas (COLT, 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 001 (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 001 (ICML, 2024)

Sequential Probability Assignment with Contexts: Minimax Regret, Contextual Shtarkov Sums, and Contextual Normalized Maximum Likelihood. - Ziyi Liu; Idan Attias; Dan Roy (NeurIPS, 2024)

Information Complexity of Stochastic Convex Optimization: Applications to Generalization and Memorization. - Idan Attias; Gintare Karolina Dziugaite; Mahdi Haghifam; Roi Livni; Daniel M. Roy 001 (CoRR, 2024)

Sequential Probability Assignment with Contexts: Minimax Regret, Contextual Shtarkov Sums, and Contextual Normalized Maximum Likelihood. - Ziyi Liu; Idan Attias; Daniel M. Roy 001 (CoRR, 2024)

Fat-Shattering Dimension of k-fold Aggregations. - Idan Attias; Aryeh Kontorovich (J. Mach. Learn. Res., 2024)

A Framework for Adversarial Streaming Via Differential Privacy and Difference Estimators. - Idan Attias; Edith Cohen; Moshe Shechner; Uri Stemmer (Algorithmica, 2024)

Sample Compression Scheme Reductions. - Idan Attias; Steve Hanneke; Arvind Ramaswami (ALT, 2025)

Capacity-Constrained Online Learning with Delays: Scheduling Frameworks and Regret Trade-offs. - Alexander Ryabchenko; Idan Attias; Daniel M. Roy 001 (COLT, 2025)

Fixed Point Computation: Beating Brute Force with Smoothed Analysis. - Idan Attias; Yuval Dagan; Constantinos Daskalakis; Rui Yao; Manolis Zampetakis (CoRR, 2025)

On the Dichotomy Between Privacy and Traceability in ℓp Stochastic Convex Optimization. - Sasha Voitovych; Mahdi Haghifam; Idan Attias; Gintare Karolina Dziugaite; Roi Livni; Daniel M. Roy 001 (CoRR, 2025)

PAC Learning with Improvements. - Idan Attias; Avrim Blum; Keziah Naggita; Donya Saless; Dravyansh Sharma; Matthew R. Walter (CoRR, 2025)

Learning-Augmented Algorithms for Boolean Satisfiability. - Idan Attias; Xing Gao; Lev Reyzin (CoRR, 2025)

Tradeoffs between Mistakes and ERM Oracle Calls in Online and Transductive Online Learning. - Idan Attias; Steve Hanneke; Arvind Ramaswami (CoRR, 2025)