arXiv Machine Learning October 9, 2026 By Timo L\"ohr, Paul Hofman, Maximilian Muschalik, Eyke H\"ullermeier

Credal Machine Learning for Risk-Averse Decision Making

Read the original on arXiv Machine Learning →

The paper introduces a method for risk‑averse machine learning that uses credal sets—collections of probability distributions—to capture epistemic uncertainty about the true loss distribution. By producing predictions as credal sets and applying a novel decision rule that selects a single predictive distribution for conditional value‑at‑risk (CVaR) minimization, the approach aims to reliably avoid catastrophic decisions across classification, distribution shift, and reinforcement learning scenarios while maintaining near‑optimal expected performance.

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