Zobrazeno 1 - 10
of 78
pro vyhledávání: '"Roquain, Étienne"'
In supervised learning, including regression and classification, conformal methods provide prediction sets for the outcome/label with finite sample coverage for any machine learning predictor. We consider here the case where such prediction sets come
Externí odkaz:
http://arxiv.org/abs/2403.12295
Conformal inference is a fundamental and versatile tool that provides distribution-free guarantees for many machine learning tasks. We consider the transductive setting, where decisions are made on a test sample of $m$ new points, giving rise to $m$
Externí odkaz:
http://arxiv.org/abs/2310.18108
We provide new non-asymptotic false discovery proportion (FDP) confidence envelopes in several multiple testing settings relevant for modern high dimensional-data methods. We revisit the multiple testing scenarios considered in the recent work of Kat
Externí odkaz:
http://arxiv.org/abs/2306.07819
This paper studies the semi-supervised novelty detection problem where a set of "typical" measurements is available to the researcher. Motivated by recent advances in multiple testing and conformal inference, we propose AdaDetect, a flexible method t
Externí odkaz:
http://arxiv.org/abs/2208.06685
The clustering task consists in partitioning elements of a sample into homogeneous groups. Most datasets contain individuals that are ambiguous and intrinsically difficult to attribute to one or another cluster. However, in practical applications, mi
Externí odkaz:
http://arxiv.org/abs/2203.02597
Valid online inference is an important problem in contemporary multiple testing research,to which various solutions have been proposed recently. It is well-known that these existing methods can suffer from a significant loss of power if the null $p$-
Externí odkaz:
http://arxiv.org/abs/2110.01255
This work investigates multiple testing by considering minimax separation rates in the sparse sequence model, when the testing risk is measured as the sum FDR+FNR (False Discovery Rate plus False Negative Rate). First using the popular beta-min separ
Externí odkaz:
http://arxiv.org/abs/2109.13601
Autor:
Mary, David, Roquain, Etienne
An important limitation of standard multiple testing procedures is that the null distribution should be known. Here, we consider a null distribution-free approach for multiple testing in the following semi-supervised setting: the user does not know t
Externí odkaz:
http://arxiv.org/abs/2106.13501
We address the multiple testing problem under the assumption that the true/false hypotheses are driven by a Hidden Markov Model (HMM), which is recognized as a fundamental setting to model multiple testing under dependence since the seminal work of \
Externí odkaz:
http://arxiv.org/abs/2105.00288
In the sparse sequence model, we consider a popular Bayesian multiple testing procedure and investigate for the first time its behaviour from the frequentist point of view. Given a spike-and-slab prior on the high-dimensional sparse unknown parameter
Externí odkaz:
http://arxiv.org/abs/2102.00929