Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
(Optimal) Online Bipartite Matching with Degree Information
Authors: Anders Aamand, Justin Chen, Piotr Indyk
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 18,518 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,401 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments We complement our theoretical studies with an extensive empirical evaluation of MPD for multiple random graph models and real graph benchmarks in Section 7. |
| Researcher Affiliation | Academia | Anders Aamand MIT EMAIL Justin Y. Chen MIT EMAIL Piotr Indyk MIT EMAIL |
| Pseudocode | Yes | Algorithm 1 Min Predicted Degree |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] See supplementary material. |
| Open Datasets | Yes | Oregon: 9 graphs sampled over 3 months representing a communication network of internet routers from the Stanford SNAP Repository [37]. |
| Dataset Splits | No | The paper describes running experiments over 100 trials with randomized online node arrival order but does not specify explicit train/validation/test dataset splits with percentages or counts. |
| Hardware Specification | Yes | The experiments were all run on a 2018 Mac Book Pro. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | In our experiments, we set size n = m = 1000, set C = m/2, and vary the exponent . and For MPD, the offline degree predictor σ : U ! R is based on the first graph: if an offline node u (i.e. a specific router) appeared in the first graph, σ(u) is the degree of u in that graph. If an offline node u did not appear in the first graph, σ(u) = 1. |