[Statlist] ETH Young Data Science Researcher Seminar Zurich, Virtual Seminar by Stephen Bates, UC Berkeley

[Maurer Letizia]

Dear all

We are glad to announce the following talk in the virtual ETH Young Data Science Researcher Seminar Zurich

"Distribution-​Free, Risk-​Controlling Prediction Sets"  
by Stephen Bates, UC Berkeley

Time: Friday, 19 March 2021, 16:00 -​17:00
Place: Zoom at https://ethz.zoom.us/j/92367940258

Abstract: To enable valid statistical inference in prediction tasks, we show how to generate set-​valued predictions with black-​box models that control various notions of statistical error. Our approach guarantees that the expected loss on future test points falls below a user-​specified level, for any predictive model and underlying distribution. Building on conformal prediction, we use a holdout set to calibrate the size of the prediction sets, generalizing the approach to control error notions such as the false rejection rate. We demonstrate our procedure in four large-​scale problems: (1) multi-​label classification, where each observation has multiple associated labels; (2) classification problems where the labels have a hierarchical structure; (3) image segmentation, where we wish to predict a set of pixels containing an object of interest; and (4) protein structure prediction.

Best wishes,

M. Azadkia, Y. Chen, G. Chinot, M. Löffler, A. Taeb

Seminar website: https://math.ethz.ch/sfs/news-and-events/young-data-science.html