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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Online Facility Location with Multiple Advice
Authors: Matteo Almanza, Flavio Chierichetti, Silvio Lattanzi, Alessandro Panconesi, Giuseppe Re
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We complement our theoretical analysis with an in-depth study of the performance of our algorithm, showing its effectiveness on synthetic and real-world data sets. |
| Researcher Affiliation | Collaboration | Matteo Almanza Dipartimento di Informatica Sapienza University Rome, Italy EMAIL Flavio Chierichetti Dipartimento di Informatica Sapienza University Rome, Italy EMAIL Silvio Lattanzi Google Research Zurich, Switzerland EMAIL Alessandro Panconesi Dipartimento di Informatica Sapienza University Rome, Italy EMAIL Giuseppe Re Dipartimento di Informatica Sapienza University Rome, Italy EMAIL |
| Pseudocode | Yes | Algorithm 1 Algorithm TAKEHEED; Algorithm 2 Algorithm PLUCK(T, v, q); Algorithm 3 Algorithm SELECTHEAVIESTCHILD(T, v, q) |
| Open Source Code | Yes | For all algorithms above we used our own implementation2. [footnote] 2https://github.com/matteojug/Online-Facility-Location-with-Multiple-Advice |
| Open Datasets | Yes | For real real-world datasets, we consider Gowalla and Brightkite, from the SNAP Dataset Collection [Leskovec and Krevl, 2014], and Uber [Five Thirty Eight, 2015]. |
| Dataset Splits | No | The paper describes how input sequences and advice were generated from daily data and time windows, but it does not specify explicit training, validation, or test dataset splits in terms of percentages or counts for model reproduction. |
| Hardware Specification | No | All experiments ran on a desktop computer. This statement is too general and does not provide specific hardware details like CPU, GPU models, or memory. |
| Software Dependencies | No | The paper mentions using 'our own implementation' but does not specify any software names with version numbers (e.g., programming languages, libraries, frameworks, or solvers with their versions). |
| Experiment Setup | Yes | The facility cost was determined in such a way that THEBASELINE opened a number of facilities between 1% 10% of the number of input points. For Fotakis and ABUV algorithms, which are parametrized, we tried different values for the parameters and report here only the best ones. The mixing algorithm has a parameter γ with which one can give more weight to one of the two components to be mixed. We show the outcome for γ = 1.75 which gave best results. |