arXiv:2505. 08908v3 Announce Type: replace-cross Abstract: Many researchers apply classical statistical decision theory to evaluate treatment choices and learn optimal policies.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2607. 21806v1 Announce Type: new Abstract: Predictive machine learning (ML) models are increasingly used to aid human decision-makers across various high-risk domains such as healthcare and criminal justice.
arXiv:2606. 02198v1 Announce Type: new Abstract: Prediction tasks over individual futures, which are inherently noisy, often admit multiple similarly accurate models.
arXiv:2607. 02206v1 Announce Type: cross Abstract: Predictions are increasingly used to guide high-stakes decisions, from treatment selection to policy making.
arXiv:2608. 13209v1 Announce Type: cross Abstract: Many operational decisions are sequences of interventions under a cumulative resource limit, such as a maintenance schedule within a crew-hour budget.
The paper introduces a method for learning risk scores that remain reliable even when historical data contain unobserved confounders. By treating propensity weights as uncertain and applying sensitivity analysis with Wasserstein distributionally robust optimization, the authors formulate a robust learning problem solvable via an exponential cone program. Experiments on semi‑synthetic UCI data show the approach improves calibration by up to 29.2% over traditional benchmarks and 11.1% over the state of the art, without harming other performance metrics.
arXiv:2607. 05620v1 Announce Type: cross Abstract: In many decision-making settings, new interventions are acceptable only if they do not reduce outcomes below some established threshold.