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..
Explainable k-Means and k-Medians Clustering
Authors: Michal Moshkovitz, Sanjoy Dasgupta, Cyrus Rashtchian, Nave Frost
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide several new theoretical results on explainable k-means and k-medians clustering. Our new algorithms and lower bounds are summarized in Table 1. |
| Researcher Affiliation | Academia | 1University of California, San Diego 2Tel Aviv University. Correspondence to: Nave Frost <EMAIL>, Michal Moshkovitz <EMAIL>, Cyrus Rashtchian <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 ITERATIVE MISTAKE MINIMIZATION |
| Open Source Code | No | The paper does not provide any links to source code or explicitly state that the code for the described methodology is open-source or publicly available. |
| Open Datasets | No | The paper uses abstract data sets (e.g., "a set of points X = {x1, . . . , xn} ∈ Rd") and provides illustrative examples (e.g., Figure 1, Figure 3) but does not mention or provide access information for any specific publicly available datasets used for empirical training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not report on empirical experiments with dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and focuses on algorithm design and analysis. It does not mention any specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and describes algorithms and proofs. It does not list any specific software dependencies with version numbers required for replication. |
| Experiment Setup | No | The paper is theoretical and presents algorithms and their guarantees. It does not detail an experimental setup including hyperparameters or training configurations for empirical evaluations. |