Fairness in Matching under Uncertainty
Authors: Siddartha Devic, David Kempe, Vatsal Sharan, Aleksandra Korolova
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To evaluate how closely the practical performance aligns with our theoretical results, and how our method compares with prior work, we ran a simple experiment which we detail in Appendix E. Figure 2 summarizes the main take-away: our method enjoys sizable utility gains across different ϕ values over a Thompson Sampling baseline. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Southern California, Los Angeles, CA 90089. 2Department of Computer Science and School of Public and International Affairs, Princeton University, Princeton, NJ 08544. Correspondence to: Siddartha Devic <devic@usc.edu>. |
| Pseudocode | No | The paper describes algorithmic steps in narrative form and as a numbered list in Section 4.2, but it does not include a formally labeled 'Algorithm' block or 'Pseudocode' figure. |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | The experiment uses a public dataset from the Czech dating site Libimseti (Brozovsky and Petricek, 2007) which has been used in experiments in prior work on matching (e.g., Su et al. (2022)). |
| Dataset Splits | No | The paper describes the dataset size ('200 users', 'two 100 x 100 matrices') and how parameters were estimated through sampling ('ran the Gale-Shapley algorithm 10,000 times'), but it does not specify any training, validation, or test dataset splits in the conventional machine learning sense. |
| Hardware Specification | No | The paper does not specify any hardware used for running the experiments (e.g., specific GPU or CPU models). |
| Software Dependencies | No | The paper mentions using a 'standard matrix completion technique' and refers to an 'LP solver' but does not provide specific software names with version numbers for reproducibility. |
| Experiment Setup | Yes | To construct the approximate RHS fairness constraints bℓin the linear program Equation (2), we ran the Gale-Shapley algorithm 10,000 times, each time with different merits sampled from the aforementioned rating distributions. We set ϵ = 0.01, so 10,000 samples is approximately sufficient to obtain error at most ϵ/2 by Proposition 9. |