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.CL2026
Towards Intrinsic Interpretability of Large Language Models:A Survey of Design Principles and Architectures
Yutong Gao, Qinglin Meng, Yuan Zhou +1
While Large Language Models (LLMs) have achieved strong performance across many NLP tasks, their opaque internal mechanisms hinder trustworthiness and safe deployment. Existing sur…
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…