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Statistics & Data Science Seminar
"Wild Refitting for Black Box Prediction"
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Speaker: Martin Wainwright (MIT) Cecil H. Green Professor EECS and Mathematics Laboratory for Information and Decision Systems Statistics and Data Science Center Institute for Data, Systems and Society Massachusetts Institute of Technology Monday, October 6, 2025 4:00PM - 5:00PM Teatime at 3:30pm in 1307
Talk at 4:00pm in 1327 Location: Yale Institute for Foundations of Data Science, Kline Tower 13th Floor, Room 1327, New Haven, CT 06511 and via Webcast: https://yale.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=a44b304c-d962-41d1-b25a-b34d00f978a8 |
Abstract: Obtaining inferential guarantees on the performance of a prediction method is essential in practice. Modern predictive methods present barriers: (a) they are opaque, so that a statistician is limited to querying its predicted values only (with no further insight into the method’s properties); (b) a severely limited number of refits, due to computational expense; and (c) data can be heterogeneous. We describe a novel procedure for estimating the excess risk of any black box regression method that overcomes these challenges, and avoids any use of hold-out. Inspired by the wild bootstrap, it uses Rademacher residual symmetrization to construct a synthetic dataset for refitting. Unlike the bootstrap, it requires only a single refit, and we give non-asymptotic guarantees on the risk estimate. We illustrate its behavior for non-rigid structure-from-motion, and plug-and-play image denoising using deep net priors.
Pre-print: https://arxiv.org/abs/2506.21460
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- Statistics & Data Science Seminar
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