| Adversarial laws of large numbers and optimal regret in online classification N Alon, O Ben-Eliezer, Y Dagan, S Moran, M Naor, E Yogev Proceedings of the 53rd annual ACM SIGACT symposium on theory of computing …, 2021 | 57 | 2021 |
| Detecting correlations with little memory and communication Y Dagan, O Shamir Conference On Learning Theory, 1145-1198, 2018 | 51 | 2018 |
| Smoothed online learning is as easy as statistical learning A Block, Y Dagan, N Golowich, A Rakhlin Conference on Learning Theory, 1716-1786, 2022 | 44 | 2022 |
| Optimality of maximum likelihood for log-concave density estimation and bounded convex regression G Kur, Y Dagan, A Rakhlin arXiv preprint arXiv:1903.05315, 2019 | 42* | 2019 |
| Learning from weakly dependent data under dobrushin’s condition Y Dagan, C Daskalakis, N Dikkala, S Jayanti Conference on Learning Theory, 914-928, 2019 | 35 | 2019 |
| Learning Ising models from one or multiple samples Y Dagan, C Daskalakis, N Dikkala, AV Kandiros Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing …, 2021 | 31* | 2021 |
| Space lower bounds for linear prediction in the streaming model Y Dagan, G Kur, O Shamir Conference on Learning Theory, 929-954, 2019 | 29 | 2019 |
| Consistent diffusion models: Mitigating sampling drift by learning to be consistent G Daras, Y Dagan, A Dimakis, C Daskalakis Advances in Neural Information Processing Systems 36, 2024 | 26 | 2024 |
| Ambient diffusion: Learning clean distributions from corrupted data G Daras, K Shah, Y Dagan, A Gollakota, A Dimakis, A Klivans Advances in Neural Information Processing Systems 36, 2024 | 24 | 2024 |
| A bounded-noise mechanism for differential privacy Y Dagan, G Kur Conference on Learning Theory, 625-661, 2022 | 24 | 2022 |
| Score-guided intermediate layer optimization: Fast Langevin mixing for inverse problems G Daras, Y Dagan, AG Dimakis, C Daskalakis arXiv preprint arXiv:2206.09104, 2022 | 21 | 2022 |
| Majorizing measures, sequential complexities, and online learning A Block, Y Dagan, A Rakhlin Conference on Learning Theory, 587-590, 2021 | 18 | 2021 |
| PAC learning with stable and private predictions Y Dagan, V Feldman Conference on Learning Theory, 1389-1410, 2020 | 17 | 2020 |
| Interaction is necessary for distributed learning with privacy or communication constraints Y Dagan, V Feldman Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing …, 2020 | 17 | 2020 |
| A better resource allocation algorithm with semi-bandit feedback Y Dagan, C Koby Algorithmic Learning Theory, 268-320, 2018 | 15 | 2018 |
| From External to Swap Regret 2.0: An Efficient Reduction for Large Action Spaces Y Dagan, C Daskalakis, M Fishelson, N Golowich Proceedings of the 56th Annual ACM Symposium on Theory of Computing, 1216-1222, 2024 | 13 | 2024 |
| Twenty (simple) questions Y Dagan, Y Filmus, A Gabizon, S Moran Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing, 9-21, 2017 | 13* | 2017 |
| Statistical estimation from dependent data V Kandiros, Y Dagan, N Dikkala, S Goel, C Daskalakis International Conference on Machine Learning, 5269-5278, 2021 | 9 | 2021 |
| Online learning and solving infinite games with an erm oracle A Assos, I Attias, Y Dagan, C Daskalakis, MK Fishelson The Thirty Sixth Annual Conference on Learning Theory, 274-324, 2023 | 8 | 2023 |
| The entropy of lies: playing twenty questions with a liar Y Dagan, Y Filmus, D Kane, S Moran arXiv preprint arXiv:1811.02177, 2018 | 6 | 2018 |
