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..
Greedy Sampling for Approximate Clustering in the Presence of Outliers
Authors: Aditya Bhaskara, Sharvaree Vadgama, Hong Xu
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we demonstrate the empirical performance of our algorithm on multiple real and synthetic datasets, and compare it to existing heuristics. |
| Researcher Affiliation | Academia | Aditya Bhaskara University of Utah EMAIL Sharvaree Vadgama University of Utah EMAIL Hong Xu University of Utah EMAIL |
| Pseudocode | Yes | Algorithm 1 k-center with outliers |
| Open Source Code | No | The paper does not provide any explicit statement or link for open-sourcing the code for the methodology described. |
| Open Datasets | Yes | All real datasets we use are available from the UCI repository [15]. |
| Dataset Splits | No | The paper mentions datasets used but does not provide specific training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not specify any particular hardware (GPU, CPU models, memory) used for the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | In order to simulate corruptions, we randomly choose 2.5% of the points in the datasets and corrupt all the coordinates by adding independent noise in a pre-deο¬ned range. |