5 papers
Explanation Quality Assessment as Ranking with Listwise Rewards
Thomas Bailleux, Tanmoy Mukherjee, Emmanuel Lonca +2
We reformulate explanation quality assessment as a ranking problem rather than a generation problem. Instead of optimizing models to produce a single "best" explanation token-by-to…
Credal Concept Bottleneck Models for Epistemic-Aleatoric Uncertainty Decomposition
Tanmoy Mukherjee, Thomas Bailleux, Pierre Marquis +1
Concept Bottleneck Models (CBMs) predict through human-interpretable concepts, but they typically output point concept probabilities that conflate epistemic uncertainty (reducible…
Probabilistic classification from possibilistic data: computing Kullback-Leibler projection with a possibility distribution
Ismaïl Baaj, Pierre Marquis
We consider learning with possibilistic supervision for multi-class classification. For each training instance, the supervision is a normalized possibility distribution that expres…
Structurally Separated Uncertainty in Supervised Latent Variable Models
Tanmoy Mukherjee, Marius Kloft, Pierre Marquis +1
Predictive uncertainty is commonly decomposed into epistemic and aleatoric components, but standard decompositions often produce strongly correlated estimates because both quantiti…
MODE: Multi-Objective Adaptive Coreset Selection
Tanmoy Mukherjee, Pierre Marquis, Zied Bouraoui
We present Mode(Multi-Objective adaptive Data Efficiency), a framework that dynamically combines coreset selection strategies based on their evolving contribution to model performa…