5 citations · 6 across the 5 of their papers we have counts for
7 papers
Mistake-Bounded Language Generation
Jon Kleinberg, Charlotte Peale, Omer Reingold
We investigate the learning task of language generation in the limit, but shift focus from the traditional time-of-last-mistake metric of a generator's success to a new notion of "…
Representative Language Generation
Charlotte Peale, Vinod Raman, Omer Reingold
We introduce "representative generation," extending the theoretical framework for generation proposed by Kleinberg et al. (2024) and formalized by Li et al. (2024), to additionally…
Multigroup Robustness
Lunjia Hu, Charlotte Peale, Judy Hanwen Shen
To address the shortcomings of real-world datasets, robust learning algorithms have been designed to overcome arbitrary and indiscriminate data corruption. However, practical proce…
Bidding Strategies for Proportional Representation in Advertisement Campaigns
Inbal Livni Navon, Charlotte Peale, Omer Reingold +1
Many companies rely on advertising platforms such as Google, Facebook, or Instagram to recruit a large and diverse applicant pool for job openings. Prior works have shown that equi…
Comparative Learning: A Sample Complexity Theory for Two Hypothesis Classes
Lunjia Hu, Charlotte Peale
In many learning theory problems, a central role is played by a hypothesis class: we might assume that the data is labeled according to a hypothesis in the class (usually referred…
Leximax Approximations and Representative Cohort Selection
Monika Henzinger, Charlotte Peale, Omer Reingold +1
Finding a representative cohort from a broad pool of candidates is a goal that arises in many contexts such as choosing governing committees and consumer panels. While there are ma…
Metric Entropy Duality and the Sample Complexity of Outcome Indistinguishability
Lunjia Hu, Charlotte Peale, Omer Reingold
We give the first sample complexity characterizations for outcome indistinguishability, a theoretical framework of machine learning recently introduced by Dwork, Kim, Reingold, Rot…