Dimension-Free Error Bounds from Random Projections
Authors: Ata Kabán4049-4056
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | 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 A.Kaban@cs.bham.ac.uk |
| 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. |