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cs.AI2026
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…
cs.AI2026
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…
cs.AI2026
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…