SW-DRSO optimizes a tractable surrogate of worst-case expected loss over plausible inference-time corruptions using a barycentric adversary approximated via simplex weights.
Prefix: Understand and adapt to user preference in human-agent interaction
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
Physician-guided feature refinement in an interactive ML framework improves delirium detection discrimination and temporal robustness over automated baselines on 3862 hospital admissions.
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Distributionally Robust Set Representation Learning Under Inference-Time Element Corruption
SW-DRSO optimizes a tractable surrogate of worst-case expected loss over plausible inference-time corruptions using a barycentric adversary approximated via simplex weights.
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Can Physician Expertise Improve Machine Learning Identification of Delirium?
Physician-guided feature refinement in an interactive ML framework improves delirium detection discrimination and temporal robustness over automated baselines on 3862 hospital admissions.