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Activity Number:
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457
- Conformal Prediction, Semiparametric Statistics, and Causal Inference
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Type:
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Invited
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Date/Time:
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Wednesday, August 10, 2022 : 2:00 PM to 3:50 PM
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Sponsor:
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IMS
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Abstract #320611
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Title:
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Testing the Stability of a Black Box Algorithm
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Author(s):
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Byol Kim and Rina Foygel Barber*
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Companies:
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University of Washington and University of Chicago
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Keywords:
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Distribution-free;
Algorithmic stability
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Abstract:
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Many results on generalization and distribution-free inference depend on the stability of a regression algorithm, which is often defined as the property that predictions on a new test point are not substantially altered by removing a single point at random from the training set. However, this stability property itself is an assumption that may not hold for highly complex predictive algorithms and/or nonsmooth data distributions. In this work we ask whether it is possible to infer the stability of an algorithm through "black box testing", where we cannot study the algorithm theoretically but instead try to determine its stability properties by the behavior of the algorithm on various data sets. Our results establish fundamental limits on the stability testing problem in the distribution-free setting.
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Authors who are presenting talks have a * after their name.
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