In this work we study the semi-supervised framework of confidence set classification with controlled expected size in minimax settings. We obtain semi-supervised minimax rates of convergence under the margin assumption and a Hölder condition on the regression function. Besides, we show that if no further assumptions are made, there is no supervised...
Frequently, population studies feature pyramidally-organized data represented using Hierarchical Bayesian Models (HBM) enriched with plates. These models can become prohibitively large in settings such as neuroimaging, where a sample is composed of a functional MRI signal measured on 64 thousand brain locations, across 4 measurement sessions, and a...
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This paper introduces the Zero and Zero-One Inflated versions of the Log Lindley distribution. Important distributional properties are investigated. Special case of both the proposed distributions are shown to be members of the exponential family. Moment methods and maximum likelihood estimation is discussed in detail with derivation of the exact e...
The ROC curve is the gold standard for measuring the performance of a test/scoring statistic regarding its capacity to discriminate between two statistical populations in a wide variety of applications, ranging from anomaly detection in signal processing to information retrieval, through medical diagnosis. Most practical performance measures used i...
Recent works have investigated deep learning models trained by optimising PAC-Bayes bounds, with priors that are learnt on subsets of the data. This combination has been shown to lead not only to accurate classifiers, but also to remarkably tight risk certificates, bearing promise towards self-certified learning (i.e. use all the data to learn a pr...
Ensuring that a predictor is not biased against a sensible feature is the key of Fairness learning. Conversely, Global Sensitivity Analysis is used in numerous contexts to monitor the influence of any feature on an output variable. We reconcile these two domains by showing how Fairness can be seen as a special framework of Global Sensitivity Analys...
INTRODUCTION : l’interprétation des intervalles de confiance et des tests d’hypothèses est généralement directionnelle, c’est-à-dire, distinguant la supériorité de l’infériorité. Par ailleurs, de nombreux outils statistiques conditionnent sur des statistiques qui sont variables, tel que la matrice d’exposition d’un modèle linéaire généralisé ou la ...
The aim of Traffic Engineering is to provide routing configurations in networks such that the used resources are minimized while maintaining a high level of quality of service (QoS). Among the optimization problems arising in this domain, we address in this paper the one related to setting weights in networks that are based on shortest path routing...
We study the minimax settings of binary classification with F-score under the $\beta$-smoothness assumptions on the regression function $\eta(x) = \mathbb{P}(Y = 1|X = x)$ for $x \in \mathbb{R}^d$. We propose a classification procedure which under the $\alpha$-margin assumption achieves the rate $O(n^{−(1+\alpha)\beta/(2\beta+d)})$ for the excess F...
