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.

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 ...

7 days ago · Omnipredictors are simple prediction functions that encode loss-minimizing predictions, simultaneously for every loss function within a ...

Apr 18, 2024 · Omnipredictors provides a framework for rigorous ... omnipredictors.pdf. Machine Learning @ Purdue. Omnipredictors. April 18, 2024. 11 / 12.

Omnipredictors. 1.2 Omnipredictors for convex loss minimization. Omnipredictors can replace perfect predictors for the sake of minimizing loss in L compared.

In Appendix H we give very efficient algorithms for solving constrained loss minimization tasks given omnipredictors. In previous work on omniprediction without ...

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 ...

Sep 9, 2024 · In a sense, omnipredictors extract all the predictive power from the class $\mathcal{C}$, irrespective of the loss function in $\mathcal{L}$. We ...

Our algorithm is based on recent work by Gopalan et al. [2023] on Omniprediction using predictors satisfying calibrated multiaccuracy. Our analysis is simple ...