Robust Generalization with Adaptive Optimal Transport Priors for Decision-Focused Learning

Haixiang Sun; Andrew L. Liu

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arXiv · 2602.01427

Robust Generalization with Adaptive Optimal Transport Priors for Decision-Focused Learning

Haixiang Sun·Andrew L. Liu

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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.

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BibTeX RIS

Haixiang Sun, Andrew L. Liu. 2026-02-01. Robust Generalization with Adaptive Optimal Transport Priors for Decision-Focused Learning. https://arxiv.org/abs/2602.01427

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