Distributionally Robust Set Representation Learning Under Inference-Time Element Corruption
Yankai Chen, Hanrong Zhang, Bowei He, Philip Yu, Xue Liu
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 the set representation and degrade performance. To address this, we propose SW-DRSO, a distributionally robust optimization framework tailored for sets. Rather than minimizing loss solely on the observed training data, SW-DRSO optimizes the worst-case expected loss over a family of plausible inference-time variations. We further introduce a barycentric adversary that transforms the intractable search for worst-case corrupted sets into a differentiable and efficient optimization process. Extensive experiments across four tasks demonstrate that SW-DRSO effectively enhances robustness against corruption while maintaining high overall performance.
BibTeX
@inproceedings{
chen2026distributionally,
title={Distributionally Robust Set Representation Learning Under Inference-Time Element Corruption},
author={Yankai Chen and Hanrong Zhang and Bowei He and Philip S. Yu and Xue Liu},
booktitle={Forty-third International Conference on Machine Learning},
year={2026},
url={https://openreview.net/forum?id=lZqQN9Ji21}
}