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..
No Dimensional Sampling Coresets for Classification
Authors: Meysam Alishahi, Jeff M. Phillips
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | While we do not provide new experimental evidence of the claims, our results are consistent with simulations in many previous papers. |
| Researcher Affiliation | Academia | 1Kahlert School of Computing, University of Utah, Salt Lake City, Utah, USA 2visiting Sca DS.AI, University of Leipzig and MPI for Math in the Sciences, Leipzig, Germany. Correspondence to: Meysam Alishahi <EMAIL>, Jeff M. Phillips <EMAIL>. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about releasing source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not report on experiments using specific datasets, thus no publicly available dataset is mentioned for training. |
| Dataset Splits | No | The paper focuses on theoretical contributions and does not include an experimental section, thus no dataset split information for validation is provided. |
| Hardware Specification | No | The paper focuses on theoretical contributions and does not conduct experiments, so no specific hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not conduct experiments, thus no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper focuses on theoretical contributions and does not include an experimental section, thus no experimental setup details like hyperparameters or training configurations are provided. |