3 papers
stat.ML2026
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…
cs.LG2026
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…
cs.LG2024
Multiclass Transductive Online Learning
Steve Hanneke, Vinod Raman, Amirreza Shaeiri +1
We consider the problem of multiclass transductive online learning when the number of labels can be unbounded. Previous works by Ben-David et al. [1997] and Hanneke et al. [2023b]…