Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Selecting Optimal Decisions via Distributionally Robust Nearest-Neighbor Regression
Authors: Ruidi Chen, Ioannis Paschalidis
NeurIPS 2019 | Venue PDF | 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 EMAIL Ioannis Ch. Paschalidis Department of Electrical and Computer Engineering Division of Systems Engineering and Department of Biomedical Engineering Boston University Boston, MA 02215 EMAIL |
| 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. |