Fair and Welfare-Efficient Constrained Multi-Matchings under Uncertainty
Authors: Elita Lobo, Justin Payan, Cyrus Cousins, Yair Zick
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare these optimization approaches empirically in Section 5 on reviewer assignment data from AAMAS 2015, 2016, and 2021. and 5 Experiments We run experiments on three reviewer assignment datasets. |
| Researcher Affiliation | Academia | Elita Lobo , Justin Payan , Cyrus Cousins, and Yair Zick University of Massachusetts Amherst {elobo, jpayan, cbcousins, yzick}@umass.edu |
| Pseudocode | No | The paper describes various algorithms (e.g., Iterated QP, Projected SGA) in text, but does not include any explicitly labeled "Pseudocode" or "Algorithm" blocks. |
| Open Source Code | Yes | All code is available at https://github.com/justinpayan/RAU2. |
| Open Datasets | Yes | We run experiments on three reviewer assignment datasets. The datasets contain bids from the International Conference on Autonomous Agents and Multiagent Systems (AAMAS) 2015, 2016, and 2021 [41, 42]. |
| Dataset Splits | No | The paper mentions setting aside a "test set" for binarized bids in Appendix E and "All results are averaged over 5 subsampling runs 20% of each dataset" in Section 5, but does not specify a full train/validation/test split with explicit percentages or sample counts for the models trained. |
| Hardware Specification | Yes | All experiments were run on Xeon E5-2680 v4 @ 2.40GHz machines with 128GB RAM with each experiment consuming at most 32 GB of memory. |
| Software Dependencies | Yes | When the valuation uncertainty set is polyhedral, the problem in (3) simplifies further into a linear program (LP) which can be solved efficiently using standard LP solvers like Gurobi [28]. |
| Experiment Setup | Yes | We optimize and evaluate CVa R0.01; we take 4, 000 samples from the distribution to optimize for CVa R using the sampling-based approach, and we take 10, 000 samples to estimate CVa R for evaluation. and For each paper a N, we set κa = κa = 3 for all a in AAMAS 2015, and κa = κa = 2 for all a in AAMAS 2016 and 2021. For each reviewer i, we set ψi = 0 and ψi = 15 for 2015 and 2016 and 4 for 2021. |