Introduction to Cis 7000 Modern Topics In Uncertainty Quantification Lecture 9
Let's dive into the details surrounding Cis 7000 Modern Topics In Uncertainty Quantification Lecture 9. We introduce the problem of conformal prediction, which reduces the problem of producing prediction sets to the problem of ...
Cis 7000 Modern Topics In Uncertainty Quantification Lecture 9 Comprehensive Overview
A bucketed definition of multicalibration for real valued predictors, and a sequential algorithm that guarantees multicalibration ... Sequential mean and quantile calibration against an adversary, beginning with a solution to the homework from last Batch Multicalibration: In sample convergence and out-of-sample generalization. We went over the case of mean multicalibration, ...
Algorithms for both mean and quantile calibration in the batch setting: they can take as input any model, and post-process the ...
Summary & Highlights for Cis 7000 Modern Topics In Uncertainty Quantification Lecture 9
- Sequential prediction with marginal quantile consistency guarantees. Offline to online reductions for mean and marginal quantile ...
- We think about using models on distributions that differ from the distributions that they have been trained on. We restrict attention ...
- Introduction to the class and marginal mean consistency.
- Finally we pay attention to features! We gave two algorithms to obtain group conditional mean consistency, for an arbitrary set of ...
- Jess Sorrell delivers a
That wraps up our extensive overview of Cis 7000 Modern Topics In Uncertainty Quantification Lecture 9.