296 citations · 530 across the 29 of their papers we have counts for
8 papers · 1 filter
Generalization in the Face of Adaptivity: A Bayesian Perspective
Moshe Shenfeld, Katrina Ligett
Repeated use of a data sample via adaptively chosen queries can rapidly lead to overfitting, wherein the empirical evaluation of queries on the sample significantly deviates from t…
Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization
Vikas K. Garg, Adam Kalai, Katrina Ligett +1
Domain generalization is the problem of machine learning when the training data and the test data come from different data domains. We present a simple theoretical model of learnin…
Gaming Helps! Learning from Strategic Interactions in Natural Dynamics
Yahav Bechavod, Katrina Ligett, Zhiwei Steven Wu +1
We consider an online regression setting in which individuals adapt to the regression model: arriving individuals are aware of the current model, and invest strategically in modify…
A New Analysis of Differential Privacy's Generalization Guarantees
Christopher Jung, Katrina Ligett, Seth Neel +3
We give a new proof of the "transfer theorem" underlying adaptive data analysis: that any mechanism for answering adaptively chosen statistical queries that is differentially priva…
Learning to Prune: Speeding up Repeated Computations
Daniel Alabi, Adam Tauman Kalai, Katrina Ligett +3
It is common to encounter situations where one must solve a sequence of similar computational problems. Running a standard algorithm with worst-case runtime guarantees on each inst…
Equal Opportunity in Online Classification with Partial Feedback
Yahav Bechavod, Katrina Ligett, Aaron Roth +2
We study an online classification problem with partial feedback in which individuals arrive one at a time from a fixed but unknown distribution, and must be classified as positive…