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..
Facility Location Problem in Differential Privacy Model Revisited
Authors: Yunus Esencayi, Marco Gaboardi, Shi Li, Di Wang
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we study the uncapacitated facility location problem in the model of differential privacy (DP) with uniform facility cost. Specifically, we first show that, under the hierarchically well-separated tree (HST) metrics and the super-set output setting that was introduced in [8], there is an ϵ-DP algorithm that achieves an O( 1ϵ ) (expected multiplicative) approximation ratio; this implies an O( log nϵ ) approximation ratio for the general metric case, where n is the size of the input metric. These bounds improve the best-known results given by [8]. |
| Researcher Affiliation | Academia | Yunus Esencayi SUNY at Buffalo EMAIL Marco Gaboardi Boston University EMAIL Shi Li SUNY at Buffalo EMAIL Di Wang SUNY at Buffalo EMAIL |
| Pseudocode | Yes | Algorithm 1 UFL-tree-base(ϵ) and Algorithm 2 DP-UFL-tree(ϵ) |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., a specific link or explicit statement of code release) for open-source code. |
| Open Datasets | No | This is a theoretical paper focused on algorithms and proofs, not empirical evaluation on datasets. Therefore, it does not mention training data availability. |
| Dataset Splits | No | This is a theoretical paper focused on algorithms and proofs, not empirical evaluation on datasets. Therefore, it does not mention validation splits. |
| Hardware Specification | No | This is a theoretical paper and does not describe experiments that would require hardware specifications. |
| Software Dependencies | No | This is a theoretical paper and does not mention specific software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper and does not describe experimental setups with hyperparameters or training configurations. |