13 papers
On Randomized Algorithms in Online Strategic Classification
Chase Hutton, Adam Melrod, Han Shao
Online strategic classification studies settings in which agents strategically modify their features to obtain favorable predictions. For example, given a classifier that determine…
A Machine Learning Theory Perspective on Strategic Litigation
Melissa Dutz, Han Shao, Avrim Blum +1
Strategic litigation involves bringing a case to court with the goal of having an impact beyond resolving the particular dispute at hand. In a common law system, one way a case may…
Pluralistic Leaderboards
Nika Haghtalab, Ariel D. Procaccia, Han Shao +2
Recent leaderboard-based evaluations of large language models aggregate user feedback by fitting a Bradley--Terry model to pairwise comparisons, producing a single global ranking b…
A Theoretical Framework for Statistical Evaluability of Generative Models
Shashaank Aiyer, Yishay Mansour, Shay Moran +1
Statistical evaluation aims to estimate the generalization performance of a model using held-out i.i.d. test data sampled from the ground-truth distribution. In supervised learning…
How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension
Cynthia Dwork, Lunjia Hu, Han Shao
We study a fundamental question of domain generalization: given a family of domains (i.e., data distributions), how many randomly sampled domains do we need to collect data from in…
Incentivized Collaboration in Active Learning
Lee Cohen, Han Shao
In collaborative active learning, where multiple agents try to learn labels from a common hypothesis, we introduce an innovative framework for incentivized collaboration. Here, rat…