26/09/2025 - Program  : 

• 9:00 - Leon Rofagha 
  “A discrimination measure under coarsening”

  Abstract: “Abstract. How does one evaluate the quality of a regression model’s predictions \(f(X_1),\ldots,f(X_n)\) under coarsening of the responses \(Y_1,\ldots,Y_n\)? A coarsening of a random variable is a random set containing said random variable. We construct a simple discrimination measure estimating the concordance index in this setup, prove its root-\(n\) asymptotic normality under an i.i.d.-sum condition for the estimated distribution function of \(Y\mid f(X_i)\), and illustrate its performance through experiments by specialising the coarsening mechanism to case-\(K\) interval-censoring.”

• 9:30 - Charlotte Jamotton 
  “A multi-criteria fair Gaussian regressor for insurance premium”

  Abstract: “This article investigates how multiple fairness criteria, such as demographic parity and proxy discrimination mitigation, can be embedded within a Gaussian Process Regression (GPR) framework. We propose a single Bayesian non-parametric model that incorporates fairness interventions through kernel design and objective function modifications. Specifically, we alter the kernel to control similarity structure (e.g., to mitigate omitted variable bias) and extend the objective beyond predictive accuracy to include fairness constraints such as enforcing independence between the premium and sensitive variables. This modified GPR architecture allows us to jointly enforce multiple fairness definitions, spanning both group and individual-level criteria, within a single model. We empirically explore trade-offs with actuarial fairness, and how different fairness criteria interact when combined. The results highlight the importance of adopting a multi-criteria, context-aware approach to fairness in insurance pricing." 

• 10:00 - Guillaume Deside 
  “Errors-in-Variables Bayesian Model of Glycemic Response to Lifestyle Factors”

  Abstract: “In recent years, the volume of data collected in clinical research has increased dramatically. This trend is driven, in part, by the proliferation of wearable medical devices and sensors, which enable both healthy and ill individuals to gather extensive longitudinal data about their health and lifestyle in situ (i.e., in the context of people’s daily lives rather than in artificial clinical environments) (Paranjape 2020). Despite their clinical potential, in situ data present statistical challenges including high missing data proportions, inaccuracies, noisier measurements than traditional clinical data, and uncertainties in event timing and individual response heterogeneity (Salathe 2024). For this presentation, we focused on the example of explaining blood glucose levels, as measured with continuous glucose monitoring (CGM) wearable sensors, using self-reported nutrient intakes and lifestyle and physiological factors. The first and simplest approach considered was to discretize the observed time-series and use linear models or random forests (RF) to roughly assess the importance of the different inputs and the shape of the glycemic responses to different factors. This approach has two important limitations. First, discretization leads to information loss that may compromise inference on parameters related to rapid glucose responses. Second, it does not account for potential timing errors in input factors, which are common in real-world health monitoring data. For example, someone may forget to report a meal or report it several hours after they actually consumed that food. Consequently, we turned to a different framework, building on Zhang et al.’s interpretable error-in-variable Bayesian model (Zhang et al 2021). Specifically, we further developed and generalized that model to include factors that were absent in the initial model and identified as important by the linear and RF models, and to account for a larger diversity of potential glycemic responses. In this presentation, I will present the results obtained from the different approaches when applied to longitudinal data from the Food&You study (Héritier et al 2023), highlight their strengths and limitations, agreements and divergences, and discuss future developments."

• 10:30 - Break

• 10:45 - Mirco Lescart 
  "A flexible sub-asymptotic model for threshold exceedances"

  Abstract : Extreme rainfall, financial crises, and structural failures often involve several variables becoming extreme at once. Standard extreme-value models capture this joint behavior only in the very far tails and usually treat dependence in a rigid way, focusing either on asymptotic dependence or on asymptotic independence. Only a few recent models allow for more flexibility between the two. I present a new sub-asymptotic model that provides a gradual transition between dependence regimes, combining ideas from generalized Pareto theory and flexible mixture representations. Estimation relies on modern likelihood-free inference through the Neural Bayes Estimator, supported by simulation studies. Finally, I illustrate how the model reveals different dependence patterns in Belgian rainfall extremes, from strong joint behavior to near-independence.

• 11:15 - Kamal Gasser  
  “Heatwave attribution over Europe”

  Abstract: “We are investigating if the rise in global mean surface temperature caused by hu-man forcing has resulted in a change in temperature distribution, hence increas-ing the frequency of heatwaves. Heatwaves manifest themselves in both spatial and temporal dimensions ; nevertheless, the majority of attribution studies con-centrate on individual locations and analyze heatwaves by calculating specific indices to capture their spatiotemporal dependency. However, this technique seems inadequate since indices do not accurately represent the real events. We aim to address this gap by using advanced statistical models and concentrating our attribution analysis on the whole of Europe. We use a clustering technique using a distance measure suitable for extremes to pinpoint spatial regions where concurrent severe temperatures occur simultaneously. Then, we formulate a flex-ible non-stationary space-time extreme value model that accommodates diverse forms of asymptotic dependency. Lastly, we use our approach to climate model outputs to estimate return period of extreme event under both a factual and counterfactual world." 

• 11:45 - Edouard Motte 
  “Signature methods in mathematical finance”

  Abstract: “The signature of a path consists of the sequence of its iterated integrals. Path-signature theory has recently emerged as a powerful tool in the fields of machine learning and mathematical finance, mainly due to its universal linearization property, according to which, provided sufficient regularity, any path functional can be expressed as a linear combination of signature elements. In this talk, I will introduce the theory of signatures and discuss some applications in mathematical finance. More specifically, I will show how signatures can be used to solve the (non-linear) stochastic control problem of hedging path-dependent options in the presence of market frictions.