Sample compression for real-valued learners S Hanneke, A Kontorovich, M Sadigurschi Algorithmic Learning Theory, 466-488, 2019 | 17 | 2019 |
Adaptive data analysis with correlated observations A Kontorovich, M Sadigurschi, U Stemmer International Conference on Machine Learning, 11483-11498, 2022 | 10 | 2022 |
On the sample complexity of privately learning axis-aligned rectangles M Sadigurschi, U Stemmer Advances in Neural Information Processing Systems 34, 28286-28297, 2021 | 8 | 2021 |
Agnostic sample compression for linear regression S Hanneke, A Kontorovich, M Sadigurschi arXiv preprint arXiv:1810.01864, 2018 | 2 | 2018 |
A Study of Privacy and Compression in Learning Theory M Sadigurschi Ben-Gurion University of the Negev, 2023 | | 2023 |
Relaxed Models for Adversarial Streaming: The Advice Model and the Bounded Interruptions Model M Sadigurschi, M Shechner, U Stemmer arXiv preprint arXiv:2301.09203, 2023 | | 2023 |
Relaxed models for adversarial streaming: The bounded interruptions model and the advice model M Sadigurschi, M Shechner, U Stemmer 31st Annual European Symposium on Algorithms (ESA 2023), 2023 | | 2023 |
Differentially-Private Bayes Consistency O Bousquet, H Kaplan, A Kontorovich, Y Mansour, S Moran, ... arXiv preprint arXiv:2212.04216, 2022 | | 2022 |
Agnostic Sample Compression Schemes for Regression I Attias, S Hanneke, A Kontorovich, M Sadigurschi arXiv e-prints, arXiv: 1810.01864, 2018 | | 2018 |
Efficient Conversion of Learners to Bounded Sample Compressors S Hanneke, A Kontorovich, M Sadigurschi Proceedings of Machine Learning Research vol 75, 1-21, 2018 | | 2018 |