5 citations · 12 across the 26 of their papers we have counts for
14 papers · 1 filter
QuadraSHAP: Stable and Scalable Shapley Values for Product Games via Gauss-Legendre Quadrature
Majid Mohammadi, Grigory Reznikov, Pavel Sinitcyn +2
We study the efficient computation of Shapley values for \emph{product games} -- cooperative games in which the coalition value factorizes as a product of per-player terms. Such ga…
Incentive Aware AI Regulations: A Credal Characterisation
Anurag Singh, Julian Rodemann, Rajeev Verma +2
The rapid proliferation of AI applications has intensified debate on effective regulation of these black-box services. Effective regulation must balance two competing goals: (1) de…
Quantifying Epistemic Predictive Uncertainty in Conformal Prediction
Siu Lun Chau, Soroush H. Zargarbashi, Yusuf Sale +1
We study the problem of quantifying epistemic predictive uncertainty (EPU) -- that is, uncertainty faced at prediction time due to the existence of multiple plausible predictive mo…
Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study
Kaizheng Wang, Yunjia Wang, Fabio Cuzzolin +3
Epistemic uncertainty in neural networks is commonly modeled using two second-order paradigms: distribution-based representations, which rely on posterior parameter distributions,…
Robust Predictive Uncertainty and Double Descent in Contaminated Bayesian Random Features
Michele Caprio, Katerina Papagiannouli, Siu Lun Chau +1
We propose a robust Bayesian formulation of random feature (RF) regression that accounts explicitly for prior and likelihood misspecification via Huber-style contamination sets. St…
Learning Credal Ensembles via Distributionally Robust Optimization
Kaizheng Wang, Ghifari Adam Faza, Fabio Cuzzolin +3
Credal predictors are models that are aware of epistemic uncertainty and produce a convex set of probabilistic predictions. They offer a principled way to quantify predictive epist…