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].
A Unified Recipe for Deriving (Time-Uniform) PAC-Bayes Bounds
Authors: Ben Chugg, Hongjian Wang, Aaditya Ramdas
JMLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present a unified framework for deriving PAC-Bayesian generalization bounds. Unlike most previous literature on this topic, our bounds are anytime-valid (i.e., time-uniform), meaning that they hold at all stopping times, not only for a fixed sample size. Our approach combines four tools in the following order: (a) nonnegative supermartingales or reverse submartingales, (b) the method of mixtures, (c) the Donsker-Varadhan formula (or other convex duality principles), and (d) Ville s inequality. Our main result is a PAC-Bayes theorem which holds for a wide class of discrete stochastic processes. We show how this result implies time-uniform versions of well-known classical PAC-Bayes bounds, such as those of Seeger, Mc Allester, Maurer, and Catoni, in addition to many recent bounds. We also present several novel bounds. |
| Researcher Affiliation | Academia | Ben Chugg EMAIL Hongjian Wang EMAIL Aaditya Ramdas EMAIL Departments of Statistics and Machine Learning Carnegie Mellon University |
| Pseudocode | No | The paper describes methods and derivations in prose and mathematical notation but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks or structured, code-like steps. |
| Open Source Code | No | The paper does not contain any explicit statements about making source code available, nor does it provide links to any code repositories. |
| Open Datasets | No | This is a theoretical paper focusing on deriving PAC-Bayesian bounds. It does not conduct empirical experiments on specific datasets, therefore, no datasets are used or made publicly available. |
| Dataset Splits | No | As this is a theoretical paper that does not perform empirical experiments using specific datasets, there is no mention of training/test/validation dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical derivations. It does not specify any software, libraries, or programming languages with version numbers required for reproduction. |
| Experiment Setup | No | This is a theoretical paper focused on deriving mathematical bounds. It does not include any experimental setup details, hyperparameters, or system-level training settings. |