Coresets for Wasserstein Distributionally Robust Optimization Problems
Authors: Ruomin Huang, Jiawei Huang, Wenjie Liu, Hu Ding
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we implement our coreset approach and illustrate its effectiveness for several WDRO problems in the experiments. |
| Researcher Affiliation | Academia | 1School of Data Science 2School of Computer Science and Technology University of Science and Technology of China 3Department of Computer Science, City University of Hong Kong |
| Pseudocode | Yes | Algorithm 1 Dual ϵ-Coreset Construction |
| Open Source Code | Yes | The code is available at https://github.com/h305142/WDRO_coreset. |
| Open Datasets | Yes | We test the algorithms for the SVM and logistic regression problems on two real datasets: MNIST[28] and LETTER[8]. The dual coreset algorithm for the robust regression problem is evaluated on the real dataset APPLIANCES ENERGY[7]. |
| Dataset Splits | No | The full settings of experiments are placed in the full version of this paper. |
| Hardware Specification | Yes | Our experiments were conducted on a server equipped with 2.4GHZ Intel CPUs and 256GB main memory. |
| Software Dependencies | Yes | We use the MOSEK [2] to solve the tractable reformulations of WDROs. [2] M. Ap S. MOSEK Optimizer API for Python 9.3.20, 2019. |
| Experiment Setup | Yes | We set c := s n to indicate the compression rate and fix the parameter γ = 7 for all the instances |