Assumption-free uncertainty quantification for black-box algorithms
Aaditya Ramdas, CMU

The fields of statistics and machine learning have made tremendous progress in the last few decades in designing accurate black-box prediction methods (boosting, random forests, bagging, neural nets, etc.) but for deployment in the real world, it would be useful to have uncertainty quantification for those point-predictions. In this tutorial, I will summarize recent work that my collaborators and I have done over the last few years, for designing a large class of such methods that work without any assumptions on the algorithm, or the distribution of the covariates, or the distribution of the labels, etc, just relying on “exchange ability” of the test and training points.

This talk is based on a sequence of joint works by the BaCaRaTi group (Rina Barber, Emmanuel Candes, myself, Ryan Tibshirani), + some recent work with a collaborator, Arun Kumar Kuchibhotla, and my student Chirag Gupta.