EE Seminar: From Learning Theory to Cryptography: Provable Guarantees for AI
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. Jonathan Shafer
MIT
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011 hall, Electrical Engineering-Kitot Building |
Wednesday, January 14th, 2026
15:00 - 16:00
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From Learning Theory to Cryptography: Provable Guarantees for AI
Abstract
Ensuring that AI systems behave as intended is a central challenge in contemporary AI. This talk offers an exposition of provable mathematical guarantees for learning and security in AI systems.
Starting with a classic learning-theoretic perspective on generalization guarantees, we present two results quantifying the amount of training data that is provably necessary and sufficient for learning: (1) In online learning, we show that access to unlabeled data can reduce the number of prediction mistakes quadratically, but no more than quadratically [NeurIPS23, NeurIPS25 Best Paper Runner-Up]. (2) In statistical learning, we discuss how much labeled data is actually necessary for learning—resolving a long-standing gap left open by the celebrated VC theorem [COLT23].
Provable guarantees are especially valuable in settings that require security in the face of malicious adversaries. The main part of the talk adopts a cryptographic perspective, showing how to: (1) Utilize interactive proof systems to delegate data collection and AI training tasks to an untrusted party [ITCS21, COLT23, NeurIPS25]. (2) Leverage random self-reducibility to provably remove backdoors from AI models, even when those backdoors are themselves provably undetectable [STOC25].
The talk concludes with an exploration of future directions concerning generalization in generative models, and AI alignment against malicious and deceptive AI.
Short Bio
Jonathan Shafer is a Postdoctoral Associate at MIT, working with Vinod Vaikuntanthan. He co-organizes the MIT ML+Crypto Seminar. Previously, he earned a PhD from UC Berkeley advised by Shafi Goldwasser.
-סמינר זה ייחשב כסמינר שמיעה לתלמידי תואר שני ושלישי-
This Seminar Is Considered A Hearing Seminar For Msc/Phd Students-
