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..
Online bipartite matching with imperfect advice
Authors: Davin Choo, Themistoklis Gouleakis, Chun Kai Ling, Arnab Bhattacharyya
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | While our contributions are mostly theoretical, we give and discuss various practical extensions of TESTANDMATCH, and also show preliminary experiments in Appendix F. |
| Researcher Affiliation | Academia | 1School of Computing, National University of Singapore 2Industrial Engineering and Operations Research, Columbia University. |
| Pseudocode | Yes | Algorithm 1 TESTANDMATCH Algorithm 2 MIMIC Algorithm 3 MINIMAXTEST Algorithm 4 SIMULATEP |
| Open Source Code | Yes | The source code is available at https://github.com/cxjdavin/ online-bipartite-matching-with-imperfect-advice. |
| Open Datasets | Yes | Our problem instances are generated from the synthetic hard known IID instance of (Manshadi et al., 2012) where any online algorithm achieves a competitive ratio of at most 0.823 in expectation. |
| Dataset Splits | No | The paper describes a generative process for instances but does not specify explicit train/validation/test splits, percentages, or absolute sample counts for data partitioning. The problem is online, so data arrives sequentially rather than being split into traditional fixed datasets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, memory, or cloud instance types) used for running the experiments. It only mentions "preliminary experiments." |
| Software Dependencies | No | The paper mentions implementing TESTANDMATCH but does not list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow, or specific solvers). |
| Experiment Setup | Yes | For our testing threshold, we set ε = ˆn/n β so that τ = 2(ˆn/n β) ε = ˆn/n β. We generated 10 random graph instances with n = 2000 offline and n online vertices. Starting with perfect advice ˆc = c , we corrupt the advice by an α parameter using two types of corruption. |