{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/selecting-optimal-decisions-via","title":"Selecting Optimal Decisions via Distributionally Robust Nearest-Neighbor Regression","arxiv_id":null,"date":"2019-12-01","proceeding":"NeurIPS 2019 12","authors":["Ruidi Chen","Ioannis Paschalidis"],"abstract":"This paper develops a prediction-based prescriptive model for optimal decision\nmaking that (i) predicts the outcome under each action using a robust\nnonlinear model, and (ii) adopts a randomized prescriptive policy determined\nby the predicted outcomes. The predictive model combines a new regularized\nregression technique, which was developed using Distributionally Robust\nOptimization (DRO) with an ambiguity set constructed from the Wasserstein\nmetric, with the K-Nearest Neighbors (K-NN) regression, which helps to\ncapture the nonlinearity embedded in the data. We show theoretical results\nthat guarantee the out-of-sample performance of the predictive model, and\nprove the optimality of the randomized policy in terms of the expected true\nfuture outcome. We demonstrate the proposed methodology on a hypertension\ndataset, showing that our prescribed treatment leads to a larger reduction in\nthe systolic blood pressure compared to a series of alternatives. A clinically\nmeaningful threshold level used to activate the randomized policy is also\nderived under a sub-Gaussian assumption on the predicted outcome.","url_abs":"http://papers.nips.cc/paper/8363-selecting-optimal-decisions-via-distributionally-robust-nearest-neighbor-regression","url_pdf":"http://papers.nips.cc/paper/8363-selecting-optimal-decisions-via-distributionally-robust-nearest-neighbor-regression.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"selecting-optimal-decisions-via","repo_url":"https://github.com/noc-lab/Select-Optimal-Decisions-via-DRO-KNN","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}