Oct 3, 2021 · Our main insight is to reframe the risk-control problem as multiple hypothesis testing, enabling techniques and mathematical arguments different ...
People also ask
Which algorithm is best for prediction?
What does calibration mean in machine learning?
This repository will allow you to reproduce the experiments in the Learn then Test paper. For now, please e-mail me if you have trouble reproducing our results.
Sep 29, 2022 · We introduce a framework for calibrating machine learning models so that their predictions satisfy explicit, finite-sample statistical ...
Abstract. We introduce Learn then Test (LTT), a framework for calibrating machine learning models so that their predictions satisfy explicit, finite-sample ...
Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control. 2021. A. N. Angelopoulos; S. Bates. A Gentle Introduction to Conformal Prediction ...
Oct 3, 2021 · A framework for calibrating machine learning models so that their predictions satisfy explicit, finite-sample statistical guarantees is ...
Jordan, Lihua Lei, "Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control," arXiv:2110.01052, 2021. About OpenReview · Hosting a Venue ...
Learn then test: Calibrating predictive algorithms to achieve risk control ... Conformal risk control. AN Angelopoulos, S Bates, A Fisch, L Lei, T Schuster.
Apr 1, 2022 · We introduce Learn then Test, a framework for calibrating machine learning models so that their predictions satisfy explicit, finite-sample ...
arXiv preprint, 2022. [arXiv] [bibtex]. “Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control”. A. Angelopoulos, S. Bates, E. Candès ...