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..
PAC-Bayes Analysis Beyond the Usual Bounds
Authors: Omar Rivasplata, Ilja Kuzborskij, Csaba Szepesvari, John Shawe-Taylor
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we discuss a basic PAC-Bayes inequality (Theorem 1 below) and a general template for PAC-Bayesian bounds (Theorem 2 below). The formulation of both these results is based on representing data-dependent distributions as stochastic kernels. |
| Researcher Affiliation | Collaboration | Omar Rivasplata University College London & Deep Mind EMAIL Ilja Kuzborskij Deep Mind EMAIL Csaba Szepesv ari Deep Mind EMAIL John Shawe-Taylor University College London EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. It presents theorems and mathematical proofs. |
| Open Source Code | No | The paper does not mention providing open-source code for the described methodology. |
| Open Datasets | No | This is a theoretical paper focusing on mathematical frameworks and theorems. It does not conduct empirical studies using specific datasets, so no dataset availability information is provided. |
| Dataset Splits | No | This is a theoretical paper and does not describe empirical experiments with training, validation, or test data splits. |
| Hardware Specification | No | This is a theoretical paper and does not describe experiments that would require hardware specifications. |
| Software Dependencies | No | This is a theoretical paper. No software dependencies with specific version numbers are mentioned as it does not describe empirical experiments. |
| Experiment Setup | No | This is a theoretical paper and does not describe empirical experiments with specific setup details or hyperparameters. |