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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…
cs.LG2026
Universal Multiclass Transductive Online Learning
Steve Hanneke, Hongao Wang
We consider the problem of universal transductive online classification with a possibly unbounded label space. This setting considers online learning, with the sequence of instance…