Media Summary: We reduce online multiobjective optimization to online linear optimization, and show that even though the minimax theorem is ... We give a broad overview of this course and attempt to make it sound interesting, important, and profound. In this lecture we give an iterative algorithm that is able to take as input an arbitrary model f, and output a more accurate model ...
Cis 6200 Learning With Conditional - Detailed Analysis & Overview
We reduce online multiobjective optimization to online linear optimization, and show that even though the minimax theorem is ... We give a broad overview of this course and attempt to make it sound interesting, important, and profound. In this lecture we give an iterative algorithm that is able to take as input an arbitrary model f, and output a more accurate model ... In this lecture we derive a simple efficient algorithm for multicalibration in sequential, adversarial settings, by specializing our ... We give an algorithm to post-process a quantile predictor to be quantile calibrated in a way that only improves its pinball loss. We analyze two calibration algorithms: An iterative one that will generalize well to satisfying other
In this class we prove basic guarantees for split conformal prediction, both in expectation over the calibration set and with high ... In this class we derive and analyze an algorithm for obtaining diminishing calibration error in a sequential adversarial ... We finish marginal conformal prediction by showing how to use our algorithm for marginal quantile consistency in the online ... We give a simple, closed form algorithm for getting regret guarantees that hold in online adversarial settings not just marginally, ... In this lecture we prove the minimax theorem by reduction to online convex optimization. We then sketch the minimax proof of a ...
