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..
A Robust Exact Algorithm for the Euclidean Bipartite Matching Problem
Authors: Akshaykumar Gattani, Sharath Raghvendra, Pouyan Shirzadian
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we present the results of our experiments comparing the execution time of our algorithm to that of the Hungarian algorithm. |
| Researcher Affiliation | Academia | Akshaykumar G. Gattani1, Sharath Raghvendra1, and Pouyan Shirzadian1 1Department of Computer Science, Virginia Tech |
| Pseudocode | Yes | The pseudo-code of our divide-and-conquer algorithm is provided in Algorithm 1. |
| Open Source Code | Yes | Our implementations are available at https://github.com/agattani190/Exact-Euclidean-Bipartite-Matching. |
| Open Datasets | Yes | For a real-world dataset, we employ the New York Taxi dataset [47] and obtain two distributions, namely (i) the distribution of pickup locations (Pickup) and (ii) the distribution of drop-off locations (Drop-off) of passengers. We filtered the datasets by considering trips in seven dates in 2014 with (i) a trip duration of at least 3 minutes, and (ii) a trip velocity of at most 110mph. ... [47] NYC Taxi and Limousine Commission (TLC). Trip record data. https://www.nyc.gov/ site/tlc/about/tlc-trip-record-data.page, 2023. Accessed: 2023-03-01. |
| Dataset Splits | No | The paper describes the synthetic and real-world datasets used and how samples were drawn but does not specify train, validation, or test splits by percentages or sample counts, nor does it mention cross-validation. |
| Hardware Specification | Yes | All computations are performed using a single calculation thread on a computer with a 2.6 GHz 6-Core Intel Core i7 CPU and 16 GB of 2667 MHz DDR4 RAM. |
| Software Dependencies | No | The paper states "Both algorithms are implemented in Java" and "we use the classical implementation of Dijkstra s shortest path algorithm" but does not provide specific version numbers for Java or any other software components. |
| Experiment Setup | Yes | For the synthetic dataset, we use two distributions, namely (i) a uniform distribution defined on the unit square (Uniform), and (ii) a Gaussian distribution constrained to the unit square with a randomly chosen mean inside the unit square and a standard deviation of 0.25 (Gaussian). For a real-world dataset, we employ the New York Taxi dataset [47] and obtain two distributions... We filtered the datasets by considering trips in seven dates in 2014 with (i) a trip duration of at least 3 minutes, and (ii) a trip velocity of at most 110mph. ... In each test, we conducted experiments using two sets of n i.i.d samples from distributions ยต and ฮฝ within the unit square. |