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..
Clustering in the Presence of Background Noise
Authors: Shai Ben-David, Nika Haghtalab
ICML 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 8, we further demonstrate the gap between these two paradigms and use experiments to confirm our theoretical results. |
| Researcher Affiliation | Academia | David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON N2L 3G1 CANADA; Computer Science Department, Carnegie Mellon University, Pittsburgh, PA 15213 USA |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper uses synthetically generated data and does not provide access information for a publicly available dataset: 'For any k, we use n = 50000 data points on the unit square. 90% of the points come from k Gaussian distributions with centers selected uniformly at random and standard deviation = 1/n. Additionally, 10% uniform noise is introduced in the data.' |
| Dataset Splits | No | The paper describes the synthetic data generation but does not provide specific dataset split information (e.g., percentages, sample counts, or explicit validation splits). |
| Hardware Specification | No | No specific hardware details (GPU/CPU models, memory, or detailed computer specifications) used for running experiments were found. |
| Software Dependencies | No | The paper mentions algorithms like Lloyd but does not provide specific software names with version numbers (e.g., library or solver names with version numbers). |
| Experiment Setup | Yes | For δ-k-means, we adapt the Lloyd algorithm to calculate the clustering using a δ-truncated distance matrix in every iteration, where δ is set to 10. |