Sep 11, 2021 · We introduce the notion of an ({\mathcal{L}},\mathcal{C})-omnipredictor, which could be used to optimize any loss in a family {\mathcal{L}}.
Sep 15, 2022 · In this paper, we introduce omnipredictors for constrained optimization and study their complexity and implications.
Apr 18, 2024 · Omnipredictors provides a framework for rigorous ... omnipredictors.pdf. Machine Learning @ Purdue. Omnipredictors. April 18, 2024. 11 / 12.
An omnipredictor for a class L of loss functions and a class C of hypotheses is a predictor whose predictions incur less expected loss than the best hypothesis ...
Omnipredictors. 1.2 Omnipredictors for convex loss minimization. Omnipredictors can replace perfect predictors for the sake of minimizing loss in L compared.
Abstract. The notion of omnipredictors (Gopalan, Kalai,. Reingold, Sharan and Wieder ITCS 2022), sug- gested a new paradigm for loss minimization.
It is shown how multicalibration can be viewed as a solution concept for agnostic boosting, shedding new light on past results and transferring insights ...
Jul 23, 2023 · Rather than learning a predictor based on a known loss function, omnipredictors can easily be post-processed to minimize any one of a rich ...
An \textit{omnipredictor} for a class L of loss functions and a class C of hypotheses is a predictor whose predictions incur less expected loss than the best ...
Duration: 36:43
Posted: Jan 28, 2022
Posted: Jan 28, 2022