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..
Dimension-Free Error Bounds from Random Projections
Authors: Ata Kabán4049-4056
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We give new, user-friendly PAC-bounds that are able to take advantage of such benign geometry to reduce dimensional-dependence of error-guarantees in settings where such dependence is known to be essential in general. This is achieved by employing random projection as an analytic tool, and exploiting its structure-preserving compression ability. |
| Researcher Affiliation | Academia | Ata Kab an School of Computer Science The University of Birmingham Edgbaston, B15 2TT Birmingham, UK EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the methodology described. |
| Open Datasets | No | The paper describes conceptual training sets (e.g., "TN = {(xn, yn)N n=1 DN d }") and input domain properties (e.g., "Xd B(0, B)"), but does not specify any publicly available datasets with access information (links, DOIs, or specific citations to existing datasets). |
| Dataset Splits | No | The paper is theoretical and focuses on generalization bounds; it does not describe experimental setups or provide details on training/validation/test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not mention any software dependencies with specific version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on deriving bounds; it does not describe an experimental setup with specific hyperparameters or training settings. |