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arXiv · 2605.30089

Distributionally Robust Set Representation Learning Under Inference-Time Element Corruption

Yankai Chen·Hanrong Zhang·Bowei He·Philip S. Yu·Xue Liu
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Abstract

Standard Set Representation Learning methods typically excel on curated data but often overlook the challenge of inference-time element corruption. This refers to scenarios where deployed models encounter element-level degradations, such as outliers or missing components, that may distort set representation and degrade performance. We propose SW-DRSO, a distributionally robust optimization framework tailored for sets. Rather than minimizing loss solely on observed training data, SW-DRSO optimizes a tractable surrogate of the worst-case expected loss over a family of plausible inference-time variations. We introduce a barycentric adversary that approximates the intractable search over corrupted sets by a differentiable training-time optimization over simplex weights. Extensive experiments across four tasks demonstrate that SW-DRSO effectively enhances robustness against corruption while maintaining high overall performance.

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Yankai Chen, Hanrong Zhang, Bowei He, Philip S. Yu, Xue Liu. 2026-05-28. Distributionally Robust Set Representation Learning Under Inference-Time Element Corruption. https://arxiv.org/abs/2605.30089

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