Generalization Error Bounds on Deep Learning with Markov Datasets
Authors: Lan V. Truong
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we derive upper bounds on generalization errors for deep neural networks with Markov datasets. These bounds are developed based on Koltchinskii and Panchenko s approach for bounding the generalization error of combined classifiers with i.i.d. datasets. The development of new symmetrization inequalities in high-dimensional probability for Markov chains is a key element in our extension, where the absolute spectral gap of the infinitesimal generator of the Markov chain plays a key parameter in these inequalities. |
| Researcher Affiliation | Academia | Lan V. Truong Department of Engineering University of Cambridge Cambridge, CB2 1PZ lt407@cam.ac.uk |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | No | The paper is theoretical and does not describe experiments using a specific dataset, nor does it provide access information for a publicly available dataset. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments, therefore no specific dataset split information for validation is provided. |
| Hardware Specification | No | The paper does not provide specific hardware details, as it is a theoretical paper and does not describe experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, as it is a theoretical paper and does not describe experiments. |
| Experiment Setup | No | The paper does not contain specific experimental setup details, as it is a theoretical paper and does not describe experiments. |