Transductive Conformal Inference With Adaptive Scores: Uniform FDP Bound for AdaDetect

Written by transduction | Published 2024/02/28
Tech Story Tags: machine-learning | conformal-inference | what-is-multiple-testing | what-is-false-discovery-rate | what-is-uniform-error-control | machine-learning-research | ml-research-papers | adaptive-scores

TLDRConformal inference is a fundamental and versatile tool that provides distribution-free guarantees for many machine learning tasks. via the TL;DR App

This paper is available on arxiv under CC 4.0 license.

Authors:

(1) Ulysse Gazin, Universit´e Paris Cit´e and Sorbonne Universit´e, CNRS, Laboratoire de Probabilit´es, Statistique et Mod´elisation,

(2) Gilles Blanchard, Universit´e Paris Saclay, Institut Math´ematique d’Orsay,

(3) Etienne Roquain, Sorbonne Universit´e and Universit´e Paris Cit´e, CNRS, Laboratoire de Probabilit´es, Statistique et Mod´elisation.

Table of Links

G Uniform FDP bound for AdaDetect

It is proved there to control the false discovery rate (FDR), defined as the mean of the FDP:

Applying Corollary 24, we obtain on the top of the in-expectation guarantee (49) the following uniform FDP bound for ADα: with probability at least 1 − δ, we have


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Published by HackerNoon on 2024/02/28