AI RESEARCH
Safe Distributionally Robust Feature Selection under Covariate Shift
arXiv CS.LG
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ArXi:2603.16062v1 Announce Type: cross In practical machine learning, the environments encountered during the model development and deployment phases often differ, especially when a model is used by many users in diverse settings. Learning models that maintain reliable performance across plausible deployment environments is known as distributionally robust (DR) learning. In this work, we study the problem of distributionally robust feature selection (DRFS), with a particular focus on sparse sensing applications motivated by industrial needs.