Optimal bounds for $\ell_p$ sensitivity sampling via $\ell_2$ augmentation
Authors: Alexander Munteanu, Simon Omlor
ICML 2024 | Conference PDF | Archive PDF | Plain Text | 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 <alexander.munteanu@tudortmund.de>, Simon Omlor <simon.omlor@tu-dortmund.de>. |
| 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. |