No Dimensional Sampling Coresets for Classification
Authors: Meysam Alishahi, Jeff M. Phillips
ICML 2024 | Conference PDF | Archive PDF | Plain Text | 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 <meysam.alishahi@utah.edu>, Jeff M. Phillips <jeffp@cs.utah.edu>. |
| 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. |