10 citations · 10 across the 6 of their papers we have counts for
6 papers
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…
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…
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…
A Theory of Optimistically Universal Online Learnability for General Concept Classes
Steve Hanneke, Hongao Wang
We provide a full characterization of the concept classes that are optimistically universally online learnable with labels. The notion of optimistically universal online…
Improving Pearson's chi-squared test: hypothesis testing of distributions -- optimally
Trung Dang, Walter McKelvie, Paul Valiant +1
Pearson's chi-squared test, from 1900, is the standard statistical tool for "hypothesis testing on distributions": namely, given samples from an unknown distribution that may o…
Maximin Fairness with Mixed Divisible and Indivisible Goods
Xiaohui Bei, Shengxin Liu, Xinhang Lu +1
We study fair resource allocation when the resources contain a mixture of divisible and indivisible goods, focusing on the well-studied fairness notion of maximin share fairness (M…