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..
Dynamic Facility Location in High Dimensional Euclidean Spaces
Authors: Sayan Bhattacharya, Gramoz Goranci, Shaofeng H.-C. Jiang, Yi Qian, Yubo Zhang
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real datasets confirm that our algorithm achieves high-quality solutions with low running time, and incurs minimal recourse. |
| Researcher Affiliation | Academia | 1University of Warwick, UK 2Faculty of Computer Science, University of Vienna, Austria 3Peking University, China. |
| Pseudocode | Yes | Algorithm 1 (Czumaj et al., 2024) on input point-set P... Algorithm 2 INSERT(p) |
| Open Source Code | No | The paper mentions 'Our implementation' but does not state it is open-source or provide a link to the code for the described methodology. |
| Open Datasets | Yes | Our experiment is done on Twitter (Chan et al., 2018b), Census1990 (Meek et al.), Covertype (Blackard, 1998) and KDD-Cup (Stolfo et al., 1999) datasets. |
| Dataset Splits | No | The paper specifies using a 'sliding window of size ℓ= 1000' but does not provide explicit training, validation, or test dataset splits (e.g., percentages or counts). |
| Hardware Specification | Yes | All the experiments are conducted on an Apple computer with M1 Pro CPU and 16GB memory. |
| Software Dependencies | No | The paper mentions implementation 'in C++ language' and use of 'standard locality-sensitive hashing (LSH) techniques' but does not specify version numbers for any software or libraries. |
| Experiment Setup | Yes | We consider a sliding window of size ℓ= 1000, and our update operations on the datasets are generated by this sliding window. The opening cost is set such that a moderate number of facilities would be open in a (near-)optimal solution. ... In our experiments, we use 15 random hash functions and we find this setup already produces decent accuracy when combining with our algorithm. |