Credal Machine Learning for Risk-Averse Decision Making
The paper introduces a method called Credal Prediction to create risk-averse machine learning models by minimizing Conditional Value-at-Risk (CVaR) instead of just average performance. It combines epistemic uncertainty with CVaR minimization using a novel decision rule that produces expressive sets, ensuring safety against worst-case scenarios in applications like reinforcement learning and classification.