Since the coined question in 1986 “Can a set of weak learners create a single strong learner?”, ensemble learning has been focused on merging simple machine learning methods in order to increase predictive performance. Characteristics such as stability and diversity are important to choose these weak learners in bagging procedure. In the same field, Support Vector Models (SVM) are strong and stable learners which have been drawing the attention of the community once these models have some properties which are easy to characterize and at the same time provide an estimation process with global optimization properties. In this talk, we present the Random Machines method. A new machine learning method based on SVM ensemble learning which exposes how a set of strong learners can create a single stronger learner.

Recent years have seen remarkable progress in Monte Carlo simulation methods, driven by the integration of cutting-edge machine learning techniques such as Generative Adversarial Networks (GANs), diffusion models, and normalizing flows. These innovations enable the generation of complex, high-dimensional data, from highly realistic human faces to artistic transformations, such as converting a landscape photo into a Van Gogh-style painting. These breakthroughs, which often make headlines, capture widespread interest but remain challenging to simulate using traditional Monte Carlo techniques. GANs operate by training two networks in a competitive framework, yielding impressive results in high-dimensional sampling. Diffusion models offer a compelling alternative to Monte Carlo sampling by iteratively refining samples, reversing a noise-adding process, and producing smooth transitions critical for many applications. Normalizing flows map simple, tractable distributions (e.g., Gaussians) to complex target distributions through a sequence of invertible transformations, enabling efficient density estimation and sample generation. These advancements significantly expand the scope of Monte Carlo simulations, allowing statisticians and researchers to model more complex and non-standard distributions with greater accuracy and computational efficiency. This talk will explore these transformative methods, highlighting their principles, applications, and potential to redefine simulation in modern statistics and data science.

Neste estudo, propomos um método diagnóstico baseado em uma nova família de funções de ligação, que permite avaliar a sensibilidade de ligações simétricas (como o logit) em relação a versões assimétricas. Essa abordagem visa melhorar o ajuste de modelos de regressão binária, especialmente em contextos médicos, onde o uso da função logit é comum devido à sua interpretabilidade via razão de chances. O modelo geral proposto, estimado por métodos baseados em verossimilhança, permite substituir a ligação padrão quando inadequada. Também desenvolvemos ferramentas de influência local sob diferentes esquemas de perturbação, com ênfase na sensibilidade da função de ligação e razão de chances. Simulações de Monte Carlo e aplicações com dados médicos ilustram a utilidade da proposta, ressaltando a importância de diagnósticos estatísticos adequados na modelagem.

La prime nette est un outil pratique de gestion des risques et l’une des mesures de risque les plus connues en assurance. Il est également connu sous le nom de principe de la valeur moyenne ($μ=E[X]$), le principe de prime le plus simple, équivalent à l’espérance de la variable de taille de sinistre (risque). Dans ce principe, le taux de prime est fixé égal à la valeur attendue du risque $X$ ($X ≥ 0$), de fonction de répartition $F$, qui est définie par \[\Pi=E[X]:=\int_0^\infty(1-F(x))\,dx\] où $Π:χ →\mathbb{R}$, appelée fonction de mesure du risque et $χ$ un ensemble de variables aléatoires réelles. En effet, il existe dans la littérature une grande variété d’estimateurs asymptotiquement normaux sur la base des données complètes.

Cependant, il arrive souvent en pratique que les données soient censurées pour une raison ou une autre de telle façon que les techniques d’estimation basées sur les données complètes deviennent inappropriées. On utilise dans ce travail la théorie des valeurs extrêmes pour proposer une estimation alternative pour les données censurées aléatoirement à droite. Sous des conditions modérées, nous établissons la normalité asymptotique de l’estimateur proposé. La preuve repose fortement sur l’approximation gaussienne de Brahmi et al. (2015), Stute (1995) et les résultats de Kaplan-Meier (1958).

^* En collaboration avec D. Meraghni et A. Necir.

Spatial confounding is a fundamental issue in spatial regression models which arises because spatial random eff ects, included to approximate unmeasured spatial variation, are typically not independent of covariates in the model. This can lead to signifi cant bias in covariate eff ect estimates. We develop a broad theoretical framework that brings mathematical clarity to the mechanisms of spatial confounding. Subsequently, we explore the potential of Bayesian methodology in alleviating spatial confounding and leveraging the understanding of how such confounding originates in the construction of prior distributions.