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..
Generalization Error Bounds on Deep Learning with Markov Datasets
Authors: Lan V. Truong
NeurIPS 2022 | Venue PDF | 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 EMAIL |
| 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. |