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..
k-Median Clustering via Metric Embedding: Towards Better Initialization with Differential Privacy
Authors: Chenglin Fan, Ping Li, Xiaoyun Li
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate the effectiveness of our proposed methods. |
| Researcher Affiliation | Industry | Chenglin Fan, Ping Li, Xiaoyun Li Cognitive Computing Lab Baidu Research 10900 NE 8th St. Bellevue, WA 98004, USA |
| Pseudocode | Yes | Algorithm 1: Local search for k-median clustering (Arya et al., 2004) [...] Algorithm 7: DP-HST local search |
| Open Source Code | No | The paper does not contain any statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | Discrete Euclidean space. Following previous work, we test k-median clustering on the MNIST hand-written digit dataset (Le Cun et al., 1998) with 10 natural clusters (digit 0 to 9). [...] The metric ρ for this experiment is the (weighted) shortest path distance. To generate a size-n graph, we first randomly split the nodes into 10 clusters. Our construction of graphs follows a similar approach as the synthetic pmedinfo graphs provided by the popular OR-Library (Beasley, 1990). |
| Dataset Splits | No | The paper describes how a 'demand set' D is sampled from a larger universe U, and specifies parameters for algorithms and repetitions for robustness, but it does not detail traditional training, validation, and test splits for the datasets themselves. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., library names, frameworks with version numbers) required to replicate the experiments. |
| Experiment Setup | Yes | For non-DP methods, we set α = 10 3 in Algorithm 1 and the maximum number of iterations as 20. For DP methods, we run the algorithms for T = 20 steps and report the results with ϵ = 1 (comparisons/results with other T and ϵ are similar). We test k {2, 5, 10, 15, 20}. The average cost over T iterations is reported for robustness. All the results are averaged over 10 independent repetitions. For non-DP tasks, we set L = 6. For DP clustering, we use L = 8. |