Selecting Optimal Decisions via Distributionally Robust Nearest-Neighbor Regression
Authors: Ruidi Chen, Ioannis Paschalidis
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the proposed methodology on a hypertension dataset, showing that our prescribed treatment leads to a larger reduction in the systolic blood pressure compared to a series of alternatives. |
| Researcher Affiliation | Academia | Ruidi Chen Division of Systems Engineering Boston University Boston, MA 02215 rchen15@bu.edu Ioannis Ch. Paschalidis Department of Electrical and Computer Engineering Division of Systems Engineering and Department of Biomedical Engineering Boston University Boston, MA 02215 yannisp@bu.edu |
| Pseudocode | Yes | Algorithm 1 Estimating the conditional mean and standard deviation of the predicted outcome. |
| Open Source Code | No | The paper does not contain any statement about releasing source code for the methodology or provide a link to a code repository. |
| Open Datasets | No | The data used for the study come from a large academic hospital system handling more than 1 million patient visits per year and consist of Electronic Health Records (EHR) containing the patients medical history in the period 1999 2014. |
| Dataset Splits | Yes | Within each prescription group, we randomly split the patient visits into three sets: a training set (80%), a validation set (10%), and a test set (10%). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper discusses various models (DRLR+K-NN, LASSO, CART, OLS+K-NN) but does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | The parameter ϵ in the threshold T(x) is set to 0.1. |