Portrait de Patrik Kenfack

Patrik Kenfack

patrik.kenfack@mila.quebec
Doctorat - École de technologie suprérieure
Superviseur⋅e principal⋅e
Ulrich Aivodji
Co-supervisor
Samira Ebrahimi Kahou
Sujets de recherche
Apprentissage profond
Équité algorithmique
Éthique de l'IA
Généralisation
IA digne de confiance
Vie privée
Site web
Google Scholar
GitHub

Publications

Training Fair Tabular Foundation Models
Patrik Kenfack
Jesse C. Cresswell
Anthony L. Caterini
Samira Ebrahimi Kahou
Ulrich Aivodji
Tabular Foundation Models (TFMs) have emerged as leading methods for tabular predictive tasks, leveraging in-context learning to predict on … (voir plus)new data without task-specific training. Despite the increased use of TFMs in high-stakes decision-making, their fairness properties remain largely unexplored. In this work, we incorporate fairness constraints directly into TFM training, enabling fair predictions in a single forward pass. Our approach addresses two key challenges: limited access to sensitive attributes in training data, and the incompatibility of existing fairness techniques with the in-context learning paradigm. We propose \ftfm{}, a scalable training strategy based on synthetic fairness tasks and a fairness-aware architecture using a gradient reversal layer, which encourages the model to learn representations invariant to sensitive attributes. Experiments on 120 fairness tasks show consistent improvements in fairness while maintaining competitive accuracy.
2026-05-24
FMSD @ International Conference on Machine Learning (présentation orale)
openreview.net
openreview.net