Knowledge-Guided Wasserstein Distributionally Robust Optimization
Authors: Zitao Wang, Ziyuan Wang, Molei Liu, Nian Si
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we present numerical simulations to validate the effectiveness of the proposed KG-WDRO method. We compare learners across different settings, including high-dimensional sparse models, correlated covariates, and multi-source prior knowledge, for either linear regression or binary classification tasks. Performance is evaluated using out-of-sample classification error for binary classifiers and out-of-sample R2 for linear regressors. |
| Researcher Affiliation | Academia | 1Department of Statistics, Columbia University, New York, USA. 2Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, USA. 3Department of Biostatistics, Peking University Health Science Center; Beijing International Center for Mathematical Research, Peking University, Beijing, China. 4Department of Industrial Engineering and Decision Analytics, Hong Kong University of Science and Technology, Hong Kong, China. |
| Pseudocode | No | The paper describes methods and theoretical results but does not contain any explicitly labeled pseudocode or algorithm blocks within its content. References are made to algorithms from other papers (e.g., "A-Trans-GLM algorithm (Tian & Feng, 2023, Algorithm 1)"), but no algorithms are presented in a structured format in this paper. |
| Open Source Code | No | The paper does not contain any explicit statement about providing source code, nor does it include a link to a code repository. The "Impact Statement" section does not address code availability. |
| Open Datasets | Yes | To demonstrate the practical applicability of our KG-WDRO framework, we evaluate it on the Trans-GLM (Tian & Feng, 2023) dataset, which compiles 2020 U.S. presidential election results at the county level (see their references for data sources). |
| Dataset Splits | Yes | Each dataset consists of three parts: data = (train, val, test). The (train, val) pair shares the same size, and hyperparameters are selected based on validation performance. The source data contains 800 samples, with source truth θ estimated accordingly. Out-of-sample performance is measured on the test set of 5000 data points. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU models, CPU types, memory specifications) used for running the experiments in the "Numerical Results" section or its appendix. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers for any libraries, frameworks, or programming languages used in the experiments. |
| Experiment Setup | Yes | Let grid1 denote a hyperparameter grid ranging from 0.0001 to 1 with 10 log-spaced values, and let grid2 denote a hyperparameter grid ranging from 0.0001 to 2 with 20 log-spaced values. The βWDRO estimator is learned by selecting the best-performing hyperparameter on grid1 using validation data. |