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stat.ML2026
Adversarially Robust PAC Learning with Optimal VC Rates
Steve Hanneke, Amirreza Shaeiri
We study the problem of \emph{adversarially robust} PAC learning. In this framework, the learner observes independent samples from an unknown distribution over $\mathcal{X} \times…
cs.LG2026
Towards a theory of inference-time alignment with unknown rewards
Steve Hanneke, Hongao Wang, Mingyue Xu
Generative model alignment has received broad interest, and significant progress has been made in supervised fine-tuning and inference-time computation. Yet, alignment has remained…
cs.LG2026
Optimal Learning Under Tsybakov Noise
Steve Hanneke, Hongao Wang, Mingyue Xu
Probably Approximately Correct (PAC) learning [Val84] is a fundamental learning model that has been extensively investigated. In this model, $\mathcal{H} \subseteq \{0,1\}^{\mathca…