AI RESEARCH
Distributionally Robust Set Representation Learning Under Inference-Time Element Corruption
arXiv CS.LG
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ArXi:2605.30089v1 Announce Type: new 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.