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..
Universal Weak Coreset
Authors: Ragesh Jaiswal, Amit Kumar
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main results include constructions of such universal weak coresets: Informal result: There is a 3-universal weak coreset for the k-MEDIAN and a 9-universal weak coreset for the k-MEANS problem in general metric spaces (the 3, 9 factors improve to 2, 4 for the special case when X F). Further, there is a 1-universal weak coreset construction for k-MEDIAN/k-MEANS in the Euclidean setting. All these weak coresets have poly( k /ε ) size. |
| Researcher Affiliation | Academia | Ragesh Jaiswal , Amit Kumar Department of Computer Science and Engineering Indian Institute of Technology Delhi EMAIL |
| Pseudocode | No | The paper describes algorithms in prose but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository links or explicit statements) for open-source code related to the described methodology. |
| Open Datasets | No | The paper is theoretical and does not conduct empirical studies with datasets, therefore it does not provide information about publicly available training datasets. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical studies, therefore it does not provide specific dataset split information (train/validation/test). |
| Hardware Specification | No | The paper is theoretical and does not report on experiments, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not report on experiments, thus no software dependencies with version numbers are listed. |
| Experiment Setup | No | The paper is theoretical and does not report on experiments, thus no specific experimental setup details like hyperparameters or training configurations are provided. |