Présidents de session
Exposé court: Applications Bio & Santé
- Nicolas Enjalbert Courrech (Institut de Mathématiques de Toulouse)
Exposé court: Machine Learning
- Sophia YAZZOURH (Institut de Mathématiques de Toulouse)
Exposé court: Compressed and distributed least-squares regression: convergence rates with applications to Federated Learning
- Constantin Philippenko (Ecole Polytechnique)
Les adventices sont des plantes qui poussent spontanément dans les parcelles agricoles et qui entrent en compétition avec les cultures. Leur dynamique repose sur la colonisation et la dormance. La banque de graines n'étant jamais observée de manière naturelle, une modélisation de cette dynamique a été proposée dans le cadre des Hidden Markov Models (HMM). Ce modèle, appelé Observation...
Generalized linear models are commonly used for modeling relationships in both univariate and multivariate contexts, with parameters traditionally estimated via the maximum likelihood estimator (MLE). MLE, while efficient, often requires a Newton-Raphson type algorithm for computation, making it time-intensive particularly with large datasets or numerous variables. Although faster, alternative...
L'un des objectifs principaux de la médecine de précision statistique est d'apprendre des règles de traitement individualisées optimales ou "Individualized Treatment Rules" (ITRs). La méthode "Outcome Weighted Learning" (OWL) propose pour la première fois, une approche basée sur la classification, ou l'apprentissage automatique, pour estimer les ITRs. Elle reformule le problème d'apprentissage...
We study the fundamental limits to the expressive power of neural networks. Given two sets F, G of real-valued functions, we first prove a general lower bound on how well functions in F can be approximated in Lp(μ) norm by functions in G, for any p≥1 and any probability measure μ. The lower bound depends on the packing number of F, the range of F, and the fat-shattering dimension of G. We then...
Adaptive importance sampling is a well-known family of algorithms for density approximation, generation and Monte Carlo integration including rare event estimation. The main common denominator of this family of algorithms is to perform density estimation with weighted samples at each iteration. However, the classical existing methods to do so, such as kernel smoothing or approximation by a...
Faced with the socio-economic challenges of flood forecasting, in a context of climate change, multi-scale modeling approaches that take advantage of the maximum amount of information available are needed to enable accurate and rapid flood forecasts.
In this context, the objective of this work is to develop a Physics-Informed Machine Learning method to efficiently perform flood models...
We investigate the impact of compression on stochastic gradient algorithms for machine learning, a technique widely used in distributed and federated learning.
We underline differences in terms of convergence rates between several unbiased compression operators, that all satisfy the same condition on their variance, thus going beyond the classical worst-case analysis. To do so, we focus on...
Current statistical methods in differential proteomics analysis generally leave aside several challenges, such as missing values, correlations between peptide intensities and uncertainty quantification. Moreover, they provide point estimates, such as the mean intensity for a given peptide or protein in a given condition. The decision of whether an analyte should be considered as differential...
