EE Seminar: Modern Challenges in Learning Theory
הרישום לסמינר יבוצע בתחילת הסמינר באמצעות סריקת הברקוד למודל (יש להיכנס לפני כן למודל, לא באמצעות האפליקציה)
Registration to the seminar is done at the beginning of the seminar by scanning the barcode for the Moodle (Please enter ahead to the Moodle, NOT by application)
(The talk will be given in English)
Speaker: Dr. Nataly Brukhim
Institute for Advanced Study (IAS) and the Center for Discrete Mathematics and Theoretical Computer Science (DIMACS)
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011 hall, Electrical Engineering-Kitot Building |
Sunday, December 28th, 2025
15:00 - 16:00
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Modern Challenges in Learning Theory
Abstract
Modern machine learning relies on its ability to generalize from limited data, yet a principled theoretical understanding of generalization remains incomplete. While binary classification is well understood in the classical PAC framework, even its natural extension to multiclass learning is substantially more challenging.
In this talk, I will present recent progress in multiclass learning that characterizes when generalization is possible and how much data is required, resolving a long-standing open problem on extending the Vapnik–Chervonenkis (VC) dimension beyond the binary setting. I will then turn to complementary results on efficient learning via boosting. We extend boosting theory to multiclass classification, while maintaining computational and statistical efficiency even for unbounded label spaces.
Lastly, I will discuss generalization in sequential learning settings, where a learner interacts with an environment over time. We introduce a new framework that subsumes classically studied settings (bandits and statistical queries) together with a combinatorial parameter that bounds the number of interactions required for learning.
Short Bio
Nataly Brukhim is a postdoctoral researcher at the Institute for Advanced Study (IAS) and the Center for Discrete Mathematics and Theoretical Computer Science (DIMACS). She received her Ph.D. in Computer Science from Princeton University, where she was advised by Elad Hazan, and was a student researcher at Google AI Princeton. She earned her M.Sc. and B.Sc. in Computer Science from Tel Aviv University.
-סמינר זה ייחשב כסמינר שמיעה לתלמידי תואר שני ושלישי-
This Seminar Is Considered A Hearing Seminar For Msc/Phd Students-
