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..
Efficient and Stable Fully Dynamic Facility Location
Authors: Sayan Bhattacharya, Silvio Lattanzi, Nikos Parotsidis
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We study the problem and provide the first algorithm that at the same time maintains a constant approximation and incurs polylogarithmic amortized recourse per update. We complement our theoretical results with an experimental analysis showing the practical efficiency of our method. |
| Researcher Affiliation | Collaboration | Sayan Bhattacharya Department of Computer Science University of Warwick Coventry, CV47AL, United Kingdom EMAIL Silvio Lattanzi Google Research EMAIL Nikos Parotsidis Google Research EMAIL |
| Pseudocode | Yes | Figure 1: FIX-CLUSTERING(.). (Contains numbered steps of an algorithm) |
| Open Source Code | Yes | All of our code is written in C++ and is available online 6. https://github.com/google-research/google-research/tree/master/fully_dynamic_facility_location |
| Open Datasets | Yes | We experiment with three classic datasets 5 from UCI library Dua and Graff [2017]: KDDCup Stolfo et al. [2000] (311, 029 points of dimension 74) and song Bertin-Mahieux et al. [2011] (515, 345 points of dimension 90) Census Kohavi et al. [1996] (2, 458, 285 points of dimension 68). |
| Dataset Splits | No | There is no training in this paper. The paper uses a sliding window model for data updates rather than traditional train/validation/test splits for model training. |
| Hardware Specification | Yes | We used a e2-standard-16 Google Cloud instance, with 16 cores, 2.20GHz Intel(R) Xeon(R) processor, and 64 Gi B main memory. |
| Software Dependencies | No | All of our code is written in C++. Specific version numbers for libraries or dependencies are not provided. |
| Experiment Setup | Yes | The behavior of our algorithm depends on two parameters: µ and ϵ. The parameter ϵ defines the base of the exponential bucketing scheme, and the parameter µ defines the level κ ij (see Invariant 3), so that (1+ϵ)κ ij µ 1 dij < (1+ϵ)κ ij µ. In Section 2.2 we set ϵ = 1 and µ = 3 for ease of analysis. ...explore setting values µ {1, 3} and ϵ {0.05, 1}. |