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[2602.01427] Robust Generalization with Adaptive Optimal Transport Priors for Decision-Focused Learning
Summary
This paper presents a Prototype-Guided Distributionally Robust Optimization (PG-DRO) framework that enhances few-shot learning by integrating class-adaptive priors for robust decision-making under distribution shifts.
Why It Matters
The research addresses the challenge of generalization in machine learning, particularly in few-shot scenarios where data is scarce. By proposing a novel framework that adapts to changing distributions, it offers a significant advancement in ensuring robust performance, which is crucial for real-world applications where data may not always be reliable or abundant.
Key Takeaways
- PG-DRO framework improves few-shot learning by using adaptive optimal transport priors.
- The method integrates class-specific knowledge to enhance decision-making robustness.
- Experiments demonstrate PG-DRO's superior performance over traditional learners and DRO baselines.
Statistics > Machine Learning arXiv:2602.01427 (stat) [Submitted on 1 Feb 2026 (v1), last revised 16 Feb 2026 (this version, v2)] Title:Robust Generalization with Adaptive Optimal Transport Priors for Decision-Focused Learning Authors:Haixiang Sun, Andrew L. Liu View a PDF of the paper titled Robust Generalization with Adaptive Optimal Transport Priors for Decision-Focused Learning, by Haixiang Sun and 1 other authors View PDF HTML (experimental) Abstract:Few-shot learning requires models to generalize under limited supervision while remaining robust to distribution shifts. Existing Sinkhorn Distributionally Robust Optimization (DRO) methods provide theoretical guarantees but rely on a fixed reference distribution, which limits their adaptability. We propose a Prototype-Guided Distributionally Robust Optimization (PG-DRO) framework that learns class-adaptive priors from abundant base data via hierarchical optimal transport and embeds them into the Sinkhorn DRO formulation. This design enables few-shot information to be organically integrated into producing class-specific robust decisions that are both theoretically grounded and efficient, and further aligns the uncertainty set with transferable structural knowledge. Experiments show that PG-DRO achieves stronger robust generalization in few-shot scenarios, outperforming both standard learners and DRO baselines. Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG); Applications (stat.AP) Cite as: arXiv:2602.01427 ...
