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.
Faster Matchings via Learned Duals
Authors: Michael Dinitz, Sungjin Im, Thomas Lavastida, Benjamin Moseley, Sergei Vassilvitskii
NeurIPS 2021 | Venue PDF | LLM Run Details | Input Tokens: 20,370 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,947 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 | We validate our theoretical findings through experiments on both real and synthetic data. In this section we present experimental results on both synthetic and real data sets. |
| Researcher Affiliation | Collaboration | Michael Dinitz Johns Hopkins University EMAIL Sungjin Im UC Merced EMAIL Thomas Lavastida Carnegie Mellon University EMAIL Benjamin Moseley Carnegie Mellon University EMAIL Sergei Vassilvitskii Google EMAIL |
| Pseudocode | Yes | Algorithm 1 Fast Approx. for Distance to Feasibility and Algorithm 2 Simple Primal-Dual Scheme for MWPM |
| 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] |
| Open Datasets | Yes | We use several datasets from the UCI Machine Learning repository [16]. See Table 1 for a summary. [16] Dheeru Dua and Casey Graff. UCI machine learning repository, 2017. URL http://archive.ics.uci.edu/ml. |
| Dataset Splits | No | For these experiments, we used 20 training instances to learn the initial duals and then tested those on 10 new instances. The paper does not explicitly provide information on a validation set or standard train/validation/test splits, only how training and test instances were used for learning the duals. |
| Hardware Specification | Yes | All of our experiments were run on Google Cloud Platform [24] e2-standard-2 virtual machines with 2 virtual CPUs and 8 GB of memory. |
| Software Dependencies | No | The paper mentions implementing 'the Hungarian Algorithm' but does not list any specific software dependencies with version numbers (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | No | The paper describes how samples are used to learn initial duals (e.g., '20 training instances', 'compute the median value'), and parameters for synthetic data generation (n, l, v), but it does not specify typical machine learning hyperparameters such as learning rates, batch sizes, or optimizer settings for the learning algorithm. |