| 2025 | ALT | Sample Compression Scheme Reductions. | Idan Attias, Steve Hanneke, Arvind Ramaswami |
| 2025 | COLT | Capacity-Constrained Online Learning with Delays: Scheduling Frameworks and Regret Trade-offs. | Alexander Ryabchenko, Idan Attias, Daniel M. Roy |
| 2025 | ICML | PAC Learning with Improvements. | Idan Attias, Avrim Blum, Keziah Naggita, Donya Saless, Dravyansh Sharma, Matthew R. Walter |
| 2024 | COLT | Universal Rates for Regression: Separations between Cut-Off and Absolute Loss. | Idan Attias, Steve Hanneke, Alkis Kalavasis, Amin Karbasi, Grigoris Velegkas |
| 2024 | ICML | Information Complexity of Stochastic Convex Optimization: Applications to Generalization, Memorization, and Tracing. | Idan Attias, Gintare Karolina Dziugaite, Mahdi Haghifam, Roi Livni, Daniel M. Roy |
| 2024 | ICML | Agnostic Sample Compression Schemes for Regression. | Idan Attias, Steve Hanneke, Aryeh Kontorovich, Menachem Sadigurschi |
| 2024 | ICML | 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 |
| 2023 | AAAI | Learning Revenue Maximization Using Posted Prices for Stochastic Strategic Patient Buyers. | Eitan-Hai Mashiah, Idan Attias, Yishay Mansour |
| 2023 | COLT | Online Learning and Solving Infinite Games with an ERM Oracle. | Angelos Assos, Idan Attias, Yuval Dagan, Constantinos Daskalakis, Maxwell K. Fishelson |
| 2023 | ICML | Adversarially Robust PAC Learnability of Real-Valued Functions. | Idan Attias, Steve Hanneke |
| 2019 | ALT | Improved Generalization Bounds for Robust Learning. | Idan Attias, Aryeh Kontorovich, Yishay Mansour |