
Generalization in the Face of Adaptivity: A Bayesian Perspective
Repeated use of a data sample via adaptively chosen queries can rapidly ...
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Causal Feature Discovery through Strategic Modification
We consider an online regression setting in which individuals adapt to t...
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Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization
Domain generalization is the problem of machine learning when the traini...
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Privately Learning Thresholds: Closing the Exponential Gap
We study the sample complexity of learning threshold functions under the...
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A New Analysis of Differential Privacy's Generalization Guarantees
We give a new proof of the "transfer theorem" underlying adaptive data a...
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A necessary and sufficient stability notion for adaptive generalization
We introduce a new notion of the stability of computations, which holds ...
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Learning to Prune: Speeding up Repeated Computations
It is common to encounter situations where one must solve a sequence of ...
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Equal Opportunity in Online Classification with Partial Feedback
We study an online classification problem with partial feedback in which...
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Access to PopulationLevel Signaling as a Source of Inequality
We identify and explore differential access to populationlevel signalin...
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ThirdParty Data Providers Ruin Simple Mechanisms
This paper studies the revenue of simple mechanisms in settings where a ...
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Learning Fair Classifiers: A RegularizationInspired Approach
We present a regularizationinspired approach for reducing bias in learn...
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Truthful Linear Regression
We consider the problem of fitting a linear model to data held by indivi...
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Katrina Ligett
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Associate Professor of Computer Science, Member, Federmann Center for the Study of Rationality at Hebrew University, Visiting Associate, Computing and Mathematical Sciences at Caltech