1 citations · 1 across the 2 of their papers we have counts for
4 papers
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…
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…