130 citations · 357 across the 15 of their papers we have counts for
18 papers
An Enriched Model of Strategic Voting under Uncertainty
Henri Surugue, Sébastien Destercke
We present a new strategic voting model where we use uncertainty representation to model preferences. Specifically, we use probability sets as uncertainty representations, together…
Upper Entropy for 2-Monotone Lower Probabilities
Tuan-Anh Vu, Sébastien Destercke, Frédéric Pichon
Uncertainty quantification is a key aspect in many tasks such as model selection/regularization, or quantifying prediction uncertainties to perform active learning or OOD detection…
Quantification of Credal Uncertainty: A Distance-Based Approach
Xabier Gonzalez-Garcia, Siu Lun Chau, Julian Rodemann +6
Credal sets, i.e., closed convex sets of probability measures, provide a natural framework to represent aleatoric and epistemic uncertainty in machine learning. Yet how to quantify…
Regret-based budgeted decision rules under severe uncertainty
Nawapon Nakharutai, Sébastien Destercke, Matthias C. M. Troffaes
One way to make decisions under uncertainty is to select an optimal option from a possible range of options, by maximizing the expected utilities derived from a probability model.…
Skeptical binary inferences in multi-label problems with sets of probabilities
Yonatan Carlos Carranza Alarcón, Sébastien Destercke
In this paper, we consider the problem of making distributionally robust, skeptical inferences for the multi-label problem, or more generally for Boolean vectors. By distributional…
Multi-label Chaining with Imprecise Probabilities
Yonatan Carlos Carranza Alarcón, Sébastien Destercke
We present two different strategies to extend the classical multi-label chaining approach to handle imprecise probability estimates. These estimates use convex sets of distribution…