Distributionally Robust Set Representation Learning Under Inference-Time Element Corruption

• URL: http://arxiv.org/abs/2605.30089v1
• Date: Thu, 28 May 2026 15:35:04 GMT
• Title: Distributionally Robust Set Representation Learning Under Inference-Time Element Corruption
• Abstract summary: We propose SW-DRSO, a distributionally robust optimization framework tailored for sets.<n>We show that SW-DRSO effectively enhances robustness against corruption while maintaining high overall performance.
• Score: 43.32606112443927
• License: http://creativecommons.org/licenses/by/4.0/
• 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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