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..
Optimal bounds for $\ell_p$ sensitivity sampling via $\ell_2$ augmentation
Authors: Alexander Munteanu, Simon Omlor
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The motivation behind our work is to find a theoretical explanation for the success of sensitivity sampling and to find out whether they also achieve the optimal complexity or if there are lower bounds preventing them from achieving optimality. |
| Researcher Affiliation | Academia | 1Dortmund Data Science Center, Faculties of Statistics and Computer Science, TU Dortmund University, Dortmund, Germany 2Faculty of Statistics, TU Dortmund University, Dortmund, Germany 3Lamarr-Institute for Machine Learning and Artificial Intelligence, Dortmund, Germany. Correspondence to: Alexander Munteanu <EMAIL>, Simon Omlor <EMAIL>. |
| Pseudocode | No | The paper focuses on theoretical proofs and mathematical derivations; it does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that source code for the described methodology is publicly available. |
| Open Datasets | No | This is a theoretical research paper that does not use datasets for empirical evaluation. |
| Dataset Splits | No | This is a theoretical research paper that does not conduct empirical experiments, and thus no dataset splits for training, validation, or testing are provided. |
| Hardware Specification | No | This is a theoretical research paper that does not conduct empirical experiments, and therefore no hardware specifications are mentioned. |
| Software Dependencies | No | This is a theoretical research paper that does not conduct empirical experiments, and therefore no specific software dependencies with version numbers are listed. |
| Experiment Setup | No | This is a theoretical research paper that does not conduct empirical experiments, and therefore no details about experimental setup or hyperparameters are provided. |