Speaker: Pratik Patil

Abstract: Prediction sets aim to trap a future observation with some guaranteed coverage. We'll visit different ways of constructing such sets, including using ideas from conformal prediction that endow such sets with nice properties. Then, we'll motivate the problem of robust prediction set estimation, in which the data generating process differs from the process generating future observation that we wish to trap. We'll see ways of adapting prediction sets to guarantee robust coverage, along with certain efficiency results for such constructions.

Relevant papers: Conformal prediction papers from Vovk's group [Shafer08, Vovk05]; use of conformal prediction ideas for (both unsupervised and supervised) prediction sets from all-star team here [Lei13, Lei15, Lei17]; robust prediction set part ongoing.