אודות
Ido Nachum joined the Department of Statistics at the University of Haifa in 2024. He began his career as an aerospace engineer in RAFAEL Ltd. (Atuda military service) while completing his MSc in pure math (2015, Technion), studying measured group theory. In his PhD (2019, Technion), he studied statistical learning questions through the lens of information theory and was a postdoctoral researcher in the School of Computer Science at EPFL, focusing on mathematical questions that arise from artificial neural computation.
פרסומים
- Minimax Limits of k-Fold Cross-Validation via Majority, Nachum, I., Urbanke, R. & Weinberger, T., 2026, In: Proceedings of Machine Learning Research. 336
- FANTASTIC GENERALIZATION MEASURES ARE NOWHERE TO BE FOUND, Gastpar, M., Nachum, I., Shafer, J. & Weinberger, T., 2024.
- Finite Littlestone Dimension Implies Finite Information Complexity, Pradeep, A., Nachum, I. & Gastpar, M., 2022, 2022 IEEE International Symposium on Information Theory, ISIT 2022. Institute of Electrical and Electronics Engineers Inc., p. 3055-3060 6 p. (IEEE International Symposium on Information Theory - Proceedings; vol. 2022-June).
- A JOHNSON-LINDENSTRAUSS FRAMEWORK FOR RANDOMLY INITIALIZED CNNS, Nachum, I., Hazła, J., Gastpar, M. & Khina, A., 2022.
- Almost-Reed-Muller Codes Achieve Constant Rates for Random Errors, Abbe, E., Hazla, J. & Nachum, I., 1 Dec 2021, In: IEEE Transactions on Information Theory. 67, 12, p. 8034-8050 17 p.
- On Symmetry and Initialization for Neural Networks, Nachum, I. & Yehudayoff, A., 2020, LATIN 2020: Theoretical Informatics - 14th Latin American Symposium 2021, Proceedings. Kohayakawa, Y. & Miyazawa, F. K. (eds.). Springer Science and Business Media Deutschland GmbH, p. 401-412 12 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 12118 LNCS).
- On the Perceptron’s Compression, Moran, S., Nachum, I., Panasoff, I. & Yehudayoff, A., 2020, Beyond the Horizon of Computability - 16th Conference on Computability in Europe, CiE 2020, Proceedings. Anselmo, M., Della Vedova, G., Manea, F. & Pauly, A. (eds.). Springer, p. 310-325 16 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 12098 LNCS).
- Average-Case Information Complexity of Learning, Nachum, I. & Yehudayoff, A., 2019, In: Proceedings of Machine Learning Research. 98, p. 633-646 14 p.
- Learners that Use Little Information, Bassily, R., Moran, S., Nachum, I., Shafer, J. & Yehudayoff, A., 2018, In: Proceedings of Machine Learning Research. 83, p. 25-55 31 p.
- A Direct Sum Result for the Information Complexity of Learning, Nachum, I., Shafer, J. & Yehudayoff, A., 2018, In: Proceedings of Machine Learning Research. 75, p. 1547-1568 22 p.