Distributionally Robust Set Representation Learning Under Inference-Time Element Corruption

arXiv:2605.30089v1 Announce Type: new 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 l

Source: arXiv cs.LG — read the full report at the original publisher.

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