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[2602.08470] Learning Credal Ensembles via Distributionally Robust Optimization
Summary
This paper presents CreDRO, a novel approach to learning credal ensembles using distributionally robust optimization, enhancing model robustness against distribution shifts.
Why It Matters
The research addresses a critical gap in understanding epistemic uncertainty in machine learning models, particularly in scenarios where distribution shifts occur. By improving the robustness of models, this work has significant implications for applications in fields like medical diagnostics and out-of-distribution detection.
Key Takeaways
- CreDRO captures epistemic uncertainty from both training randomness and distribution shifts.
- The method outperforms existing credal approaches in various benchmarks.
- It offers a principled framework for quantifying predictive uncertainty.
- The research highlights the importance of robust optimization in machine learning.
- Applications include selective classification and out-of-distribution detection.
Computer Science > Machine Learning arXiv:2602.08470 (cs) [Submitted on 9 Feb 2026 (v1), last revised 26 Feb 2026 (this version, v2)] Title:Learning Credal Ensembles via Distributionally Robust Optimization Authors:Kaizheng Wang, Ghifari Adam Faza, Fabio Cuzzolin, Siu Lun Chau, David Moens, Hans Hallez View a PDF of the paper titled Learning Credal Ensembles via Distributionally Robust Optimization, by Kaizheng Wang and 5 other authors View PDF HTML (experimental) Abstract: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 epistemic uncertainty (EU) and have been shown to improve model robustness in various settings. However, most state-of-the-art methods mainly define EU as disagreement caused by random training initializations, which mostly reflects sensitivity to optimization randomness rather than uncertainty from deeper sources. To address this, we define EU as disagreement among models trained with varying relaxations of the i.i.d. assumption between training and test data. Based on this idea, we propose CreDRO, which learns an ensemble of plausible models through distributionally robust optimization. As a result, CreDRO captures EU not only from training randomness but also from meaningful disagreement due to potential distribution shifts between training and test data. Empirical results show that CreDRO consistently outperforms existing credal...
