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 [1].
Fast-rate PAC-Bayes Generalization Bounds via Shifted Rademacher Processes
Authors: Jun Yang, Shengyang Sun, Daniel M. Roy
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The goal of this paper is to extend this bridge between Rademacher complexity and state-of-the-art PACBayesian theory. We first demonstrate that one can match the fast rate of Catoni s PAC-Bayes bounds [8] using shifted Rademacher processes [27, 43, 44]. We then derive a new fast-rate PAC-Bayes bound in terms of the flatness of the empirical risk surface on which the posterior concentrates. Our analysis establishes a new framework for deriving fast-rate PAC-Bayes bounds and yields new insights on PAC-Bayesian theory. |
| Researcher Affiliation | Academia | Jun Yang Department of Statistical Sciences University of Toronto, Vector Institute EMAIL Shengyang Sun Department of Computer Science University of Toronto, Vector Institute EMAIL Daniel M. Roy Department of Statistical Sciences University of Toronto, Vector Institute EMAIL |
| Pseudocode | No | The paper focuses on mathematical derivations and theoretical framework, and does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on specific datasets, thus no training dataset information is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with dataset splits, thus no validation split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations. |