Fat-Shattering Dimension of k-fold Aggregations
Authors: Idan Attias, Aryeh Kontorovich
JMLR 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide estimates on the fat-shattering dimension of aggregation rules of real-valued function classes. The latter consists of all ways of choosing k functions, one from each of the k classes, and computing pointwise an aggregate function of these, such as the median, mean, and maximum. The bounds are stated in terms of the fat-shattering dimensions of the component classes. |
| Researcher Affiliation | Academia | Idan Attias EMAIL Aryeh Kontorovich EMAIL Department of Computer Science Ben-Gurion University of the Negev, Beer Sheva, Israel |
| Pseudocode | No | The paper focuses on theoretical results, including definitions, theorems, lemmas, and proofs related to the fat-shattering dimension and aggregation rules. It does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements regarding the release of source code, nor does it provide links to any code repositories or mention code in supplementary materials. |
| Open Datasets | No | This paper is theoretical in nature, focusing on mathematical bounds and dimensions of function classes. It does not present empirical studies or use specific datasets, therefore, no information regarding open datasets is provided. |
| Dataset Splits | No | As this paper is theoretical and does not conduct experiments on datasets, there is no information provided regarding dataset splits. |
| Hardware Specification | No | This is a theoretical paper providing mathematical proofs and analyses. There is no experimental section, and thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any experimental implementation. Therefore, no software dependencies or their versions are specified. |
| Experiment Setup | No | The paper is focused on theoretical mathematical results. It does not include an experimental section, thus no experimental setup details, hyperparameters, or training configurations are provided. |