• Optimal Learners for Realizable Regression: PAC Learning and Online Learning

Idan Attias, Steve Hanneke, Alkis Kalavasis, Amin Karbasi, Grigoris Velegkas

37th Annual Conference on Neural Information Processing Systems (NeurIPS 2023)

Oral presentation

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• Online Learning and Solving Infinite Games with an ERM Oracle

Angelos Assos, Idan Attias, Yuval Dagan, Constantinos Daskalakis, Maxwell Fishelson

36th Annual Conference on Learning Theory (COLT 2023)

 

• Adversarially Robust Learning of Real-Valued Functions 

Idan Attias, Steve Hanneke

40th International Conference on Machine Learning (ICML 2023)

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• Learning Revenue Maximization using Posted Prices for Stochastic Strategic Patient Buyers

Eitan-Hai Mashiah, Idan Attias, Yishay Mansour

37th Conference on Artificial Intelligence (AAAI 2023)

Oral presentation

 

• A Framework for Adversarial Streaming via Differential Privacy and Difference Estimators

Idan Attias, Edith Cohen, Moshe Shechner, Uri Stemmer

14th Innovations in Theoretical Computer Science (ITCS 2023)

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• A Characterization of Semi-Supervised Adversarially-Robust PAC Learnability​

​Idan Attias, Steve Hanneke, Yishay Mansour

36th Annual Conference on Neural Information Processing Systems (NeurIPS 2022)

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• Domain Invariant Adversarial Learning

*Matan Levi, Idan Attias, Aryeh Kontorovich

Transactions on Machine Learning Research (TMLR 2022)

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• Prediction with Corrupted Expert Advice

Idan Amir, Idan Attias, Tomer Koren, Roi Livni, Yishay Mansour

34th Annual Conference on Neural Information Processing Systems (NeurIPS 2020)

Spotlight presentation, ​top 3%

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• Improved Generalization Bounds for Adversarially Robust Learning​

Idan Attias, Aryeh Kontorovich, Yishay Mansour

Journal of Machine Learning Research, Vol. 23 (JMLR 2022)

Preliminary version: 30th International Conference on Algorithmic Learning Theory (ALT 2019) 

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