7 papers
PVeRA: Probabilistic Vector-Based Random Matrix Adaptation
Leo Fillioux, Enzo Ferrante, Paul-Henry Cournède +2
Large foundation models have emerged in the last years and are pushing performance boundaries for a variety of tasks. Training or even finetuning such models demands vast datasets…
Are foundation models for computer vision good conformal predictors?
Leo Fillioux, Julio Silva-RodrÃguez, Ismail Ben Ayed +4
Recent advances in self-supervision and contrastive learning have brought the performance of foundation models to unprecedented levels in a variety of tasks. Fueled by this progres…
Evaluation of machine-learning models to measure individualized treatment effects from randomized clinical trial data with time-to-event outcomes
Elvire Roblin, Paul-Henry Cournède, Stefan Michiels
Objective: In randomized clinical trials, prediction models can be used to explore the relationships between patients' variables (e.g., clinical, pathological, or lifestyle variabl…
SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning
Leo Fillioux, Omprakash Chakraborty, Ismail Ben Ayed +4
With the increasing adoption of vision-language models (VLMs) in critical decision-making systems such as healthcare or autonomous driving, the calibration of their uncertainty est…
Causal Dynamic Variational Autoencoder for Counterfactual Regression in Longitudinal Data
Mouad El Bouchattaoui, Myriam Tami, Benoit Lepetit +1
Accurately estimating treatment effects over time is crucial in fields such as precision medicine, epidemiology, economics, and marketing. Many current methods for estimating treat…
Full Conformal Adaptation of Medical Vision-Language Models
Julio Silva-RodrÃguez, Leo Fillioux, Paul-Henry Cournède +4
Vision-language models (VLMs) pre-trained at large scale have shown unprecedented transferability capabilities and are being progressively integrated into medical image analysis. A…