Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Fair and Welfare-Efficient Constrained Multi-Matchings under Uncertainty
Authors: Elita Lobo, Justin Payan, Cyrus Cousins, Yair Zick
NeurIPS 2024 | Venue PDF | 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 EMAIL |
| 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. |