2 papers
stat.ML2026
JUCAL: Jointly Calibrating Aleatoric and Epistemic Uncertainty in Classification Tasks
Jakob Heiss, Sören Lambrecht, Jakob Weissteiner +4
We study post-calibration uncertainty for trained ensembles of classifiers. Specifically, we consider both aleatoric (label noise) and epistemic (model) uncertainty. Among the most…
q-fin.CP2025
Robust Utility Optimization via a GAN Approach
Florian Krach, Josef Teichmann, Hanna Wutte
Robust utility optimization enables an investor to deal with market uncertainty in a structured way, with the goal of maximizing the worst-case outcome. In this work, we propose a…