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..
Consistent k-Clustering
Authors: Silvio Lattanzi, Sergei Vassilvitskii
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we show experimentally that our approach performs much better than the theoretical bound, with the number of changes growing approximately as O(log n). |
| Researcher Affiliation | Industry | 1Google, Zurich, Switzerland 2Google, New York, New York, USA. |
| Pseudocode | Yes | Algorithm 1: Single Meyerson sketch; Algorithm 2: Compute Meyerson(Xt, φ); Algorithm 3: Update Meyerson(M1, . . . , Ms, xt, φ); Algorithm 4: Create Weighted Instance(M1, . . . , Ms, φ, Xt); Algorithm 5: Update Weights(M, w, x); Algorithm 6: Consistent k-clustering algorithm |
| Open Source Code | No | The paper does not provide any specific links or statements indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We evaluate our algorithm on three datasets from the UCI Repository (Lichman, 2013) that vary in data size and dimensionality. ... UCI machine learning repository, 2013. URL http://archive.ics.uci.edu/ml. |
| Dataset Splits | No | The paper mentions evaluating on datasets but does not specify train/validation/test splits, percentages, or sample counts for these datasets. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper mentions using k-means++ and a local search algorithm, but it does not specify software names with version numbers for reproducibility. |
| Experiment Setup | No | The paper describes the datasets and some algorithm modifications, but it does not provide specific experimental setup details such as hyperparameters or system-level training settings. |