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 [1].
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. |