5 citations · 5 across the 7 of their papers we have counts for
6 papers · 1 filter
Tailoring Strictly Proper Scoring Rules for Downstream Tasks: An Application to Causal Inference
Roman Plaud, Alexandre Perez-Lebel, Antoine Saillenfest +4
Probabilistic models are typically trained using task-agnostic objectives like log-loss, which can lead to significant errors in downstream estimation. This disconnect is especiall…
Table Foundation Models: on knowledge pre-training for tabular learning
Myung Jun Kim, Félix Lefebvre, Gaëtan Brison +2
Table foundation models bring high hopes to data science: pre-trained on tabular data to embark knowledge or priors, they should facilitate downstream tasks on tables. One specific…
To Each Metric Its Decoding: Post-Hoc Optimal Decision Rules of Probabilistic Hierarchical Classifiers
Roman Plaud, Alexandre Perez-Lebel, Matthieu Labeau +2
Hierarchical classification offers an approach to incorporate the concept of mistake severity by leveraging a structured, labeled hierarchy. However, decoding in such settings freq…
Decision from Suboptimal Classifiers: Excess Risk Pre- and Post-Calibration
Alexandre Perez-Lebel, Gael Varoquaux, Sanmi Koyejo +2
Probabilistic classifiers are central for making informed decisions under uncertainty. Based on the maximum expected utility principle, optimal decision rules can be derived using…
Beyond calibration: estimating the grouping loss of modern neural networks
Alexandre Perez-Lebel, Marine Le Morvan, Gaël Varoquaux
The ability to ensure that a classifier gives reliable confidence scores is essential to ensure informed decision-making. To this end, recent work has focused on miscalibration, i.…
Benchmarking missing-values approaches for predictive models on health databases
Alexandre Perez-Lebel, Gaël Varoquaux, Marine Le Morvan +2
BACKGROUND: As databases grow larger, it becomes harder to fully control their collection, and they frequently come with missing values: incomplete observations. These large databa…