Nearly-tight Bounds for Deep Kernel Learning
Authors: Yifan Zhang, Min-Ling Zhang
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | we develop an analysis method based on the composite relationship of function classes and derive capacity-based bounds with mild dependence on the depth, which generalizes learning theory bounds to deep kernels and serves as theoretical guarantees for the generalization of DKL. In this paper, we prove novel and nearly-tight generalization bounds based on the uniform covering number and the Rademacher chaos complexity for deep (multiple) kernel machines. |
| Researcher Affiliation | Academia | 1School of Cyber Science and Engineering, Southeast University, Nanjing 210096, China 2Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, China 3School of Computer Science and Engineering, Southeast University, Nanjing 210096, China. Correspondence to: Min-Ling Zhang <zhangml@seu.edu.cn>. |
| Pseudocode | No | The paper does not contain any sections or figures explicitly labeled as 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | No | The paper defines a generic dataset 'D' for theoretical analysis but does not specify a named public dataset or provide access information for any dataset used in experiments. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments, thus no training/validation/test dataset splits are provided. |
| Hardware Specification | No | The paper does not report any computational experiments, and therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe empirical experiments, thus no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training configurations. |