1 citations · 1 across the 17 of their papers we have counts for
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Uniform convergence at optimal rate and scale
Noga Alon, Steve Hanneke, Shay Moran +1
Uniform convergence asks how many random samples suffice for empirical averages to approximate expectations simultaneously over an entire class of functions. It is a central concep…
PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting
Steve Hanneke, Qinglin Meng, Shay Moran +1
We study the problem of multiclass PAC learning with bandit feedback in the realizable setting. In this framework, there is an unknown data distribution over an instance space $\ma…
On the Learning Curves of Revenue Maximization
Steve Hanneke, Alkis Kalavasis, Shay Moran +1
Learning curves are a fundamental primitive in supervised learning, describing how an algorithm's performance improves with more data and providing a quantitative measure of its ge…
Sample Complexity of Autoregressive Reasoning: Chain-of-Thought vs. End-to-End
Steve Hanneke, Idan Mehalel, Shay Moran
Modern large language models generate text autoregressively, producing tokens one at a time. To study the learnability of such systems, Joshi et al. (COLT 2025) introduced a PAC-le…
An Optimal Sauer Lemma Over -ary Alphabets
Steve Hanneke, Qinglin Meng, Shay Moran +1
The Sauer-Shelah-Perles Lemma is a cornerstone of combinatorics and learning theory, bounding the size of a binary hypothesis class in terms of its Vapnik-Chervonenkis (VC) dimensi…
We Should Separate Memorization from Copyright
Adi Haviv, Niva Elkin-Koren, Uri Hacohen +2
The widespread use of foundation models has introduced a new risk factor of copyright issue. This issue is leading to an active, lively and on-going debate amongst the data-science…
A Theory of Universal Agnostic Learning
Steve Hanneke, Shay Moran
We provide a complete theory of optimal universal rates for binary classification in the agnostic setting. This extends the realizable-case theory of Bousquet, Hanneke, Moran, van…