Scalable Distributional Robustness in a Class of Non-Convex Optimization with Guarantees
Authors: Avinandan Bose, Arunesh Sinha, Tien Mai
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally compare our different approaches and baselines, and reveal nuanced properties of a DRO solution. Our final contribution is detailed experiments validating the scalability of our approaches on a simulated security game problem as well as two variants of facility location using park and ride data-sets from New York [Holguin-Veras et al., 2012]. |
| Researcher Affiliation | Academia | Avinandan Bose University of Washington avibose@cs.washington.edu Arunesh Sinha Rutgers University arunesh.sinha@rutgers.edu Tien Mai Singapore Management University atmai@smu.edu.sg |
| Pseudocode | No | The paper contains mathematical formulations and transformations but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The ethics checklist states 'Yes' to including code, data, and instructions, but the paper itself does not provide a specific URL or clear statement of where the code for the methodology can be accessed (e.g., in supplementary material or a public repository). |
| Open Datasets | Yes | P&R-NYC Dataset : We use a large and challenging Park-and-ride (P&R) dataset collected in New York City, which provides utilities for 82341 clients (N) for 59 park and ride locations (M), along with their incumbent utilities for competing facilities [Holguin-Veras et al., 2012]; this data was directly used for MC-FLP. |
| Dataset Splits | No | The paper states 'We split the data (randomly) into training and test (80:20)' but does not explicitly mention a validation set or its split percentage. |
| Hardware Specification | Yes | We use a 2.1 GHz CPU with 128GB RAM. |
| Software Dependencies | No | The paper mentions 'CPLEX' as a solver but does not provide a specific version number for it or any other software dependencies. |
| Experiment Setup | Yes | We fix K = 10 in approximation via discretization as we find that objective increase saturates for this K (see Appendix K). The numbers reported for our baselines are the best values over 10 random initializations. We use our clustering approach with 50 clusters. |