2 citations · 2 across the 4 of their papers we have counts for
5 papers
SurvICL: a Foundation Model for in-context Survival Analysis
Julie Alberge, Linus Bleistein, Judith Abécassis +3
Tabular foundation models (TFMs) now outperform gradient-boosted trees in classification and regression. Yet in survival analysis, which predicts when an event occurs from censored…
On the calibration of survival models with competing risks
Julie Alberge, Tristan Haugomat, Gaël Varoquaux +1
Survival analysis deals with modeling the time until an event occurs, and accurate probability estimates are crucial for decision-making, particularly in the competing-risks settin…
Double Debiased Machine Learning for Mediation Analysis with Continuous Treatments
Houssam Zenati, Judith Abécassis, Julie Josse +1
Uncovering causal mediation effects is of significant value to practitioners seeking to isolate the direct treatment effect from the potential mediated effect. We propose a double…
Survival Models: Proper Scoring Rule and Stochastic Optimization with Competing Risks
Julie Alberge, Vincent Maladière, Olivier Grisel +2
When dealing with right-censored data, where some outcomes are missing due to a limited observation period, survival analysis -- known as time-to-event analysis -- focuses on predi…
Teaching Models To Survive: Proper Scoring Rule and Stochastic Optimization with Competing Risks
Julie Alberge, Vincent Maladière, Olivier Grisel +2
When data are right-censored, i.e. some outcomes are missing due to a limited period of observation, survival analysis can compute the "time to event". Multiple classes of outcomes…