Idan Attias

According to our database, Idan Attias authored at least 20 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

A Framework for Adversarial Streaming Via Differential Privacy and Difference Estimators.

Idan Attias, Edith Cohen, Moshe Shechner, Uri Stemmer

Algorithmica, November, 2024

Fat-Shattering Dimension of k-fold Aggregations.

Idan Attias, Aryeh Kontorovich

J. Mach. Learn. Res., 2024

Sequential Probability Assignment with Contexts: Minimax Regret, Contextual Shtarkov Sums, and Contextual Normalized Maximum Likelihood.

Ziyi Liu, Idan Attias, Daniel M. Roy

CoRR, 2024

Information Complexity of Stochastic Convex Optimization: Applications to Generalization and Memorization.

Idan Attias, Gintare Karolina Dziugaite, Mahdi Haghifam, Roi Livni, Daniel M. Roy

CoRR, 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

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Agnostic Sample Compression Schemes for Regression.

Idan Attias, Steve Hanneke, Aryeh Kontorovich, Menachem Sadigurschi

Proceedings of the Forty-first International Conference on Machine Learning, 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

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Universal Rates for Regression: Separations between Cut-Off and Absolute Loss.

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

Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

2023

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

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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Adversarially Robust PAC Learnability of Real-Valued Functions.

Idan Attias, Steve Hanneke

Proceedings of the International Conference on Machine Learning, 2023

Online Learning and Solving Infinite Games with an ERM Oracle.

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

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

Learning Revenue Maximization Using Posted Prices for Stochastic Strategic Patient Buyers.

Eitan-Hai Mashiah, Idan Attias, Yishay Mansour

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Domain Invariant Adversarial Learning.

Matan Levi, Idan Attias, Aryeh Kontorovich

Trans. Mach. Learn. Res., 2022

Improved Generalization Bounds for Adversarially Robust Learning.

Idan Attias, Aryeh Kontorovich, Yishay Mansour

J. Mach. Learn. Res., 2022

Adversarially Robust Learning of Real-Valued Functions.

Idan Attias, Steve Hanneke

CoRR, 2022

Stochastic Strategic Patient Buyers: Revenue maximization using posted prices.

Eitan-Hai Mashiah, Idan Attias, Yishay Mansour

CoRR, 2022

A Characterization of Semi-Supervised Adversarially Robust PAC Learnability.

Idan Attias, Steve Hanneke, Yishay Mansour

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Fat-shattering dimension of k-fold maxima.

Aryeh Kontorovich, Idan Attias

CoRR, 2021

2020

Prediction with Corrupted Expert Advice.

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

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Improved Generalization Bounds for Robust Learning.

Idan Attias, Aryeh Kontorovich, Yishay Mansour

Proceedings of the Algorithmic Learning Theory, 2019