PAC-Bayes Learning Bounds for Sample-Dependent Priors
Authors: Pranjal Awasthi, Satyen Kale, Stefani Karp, Mehryar Mohri
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present a series of new PAC-Bayes learning guarantees for randomized algorithms with sample-dependent priors. Our most general bounds make no assumption on the priors and are given in terms of certain covering numbers under the infinite-Rényi divergence and the ℓ1 distance. We show how to use these general bounds to derive learning bounds in the setting where the sample-dependent priors obey an infinite-Rényi divergence or ℓ1-distance sensitivity condition. We also provide a flexible framework for computing PAC-Bayes bounds, under certain stability assumptions on the sample-dependent priors, and show how to use this framework to give more refined bounds when the priors satisfy an infinite-Rényi divergence sensitivity condition. |
| Researcher Affiliation | Collaboration | Pranjal Awasthi Google Research and Rutgers University pranjalawasthi@google.com Satyen Kale Google Research satyenkale@google.com Stefani Karp Google Research and Carnegie Mellon University stefanik@google.com Mehryar Mohri Google Research and Courant Institute of Mathematical Sciences mohri@google.com |
| 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. |
| Open Datasets | No | The paper is theoretical and does not mention any datasets used for training or public availability of such datasets. |
| Dataset Splits | No | The paper is theoretical and does not describe any dataset splits (training, validation, test). |
| Hardware Specification | No | The paper is theoretical and does not describe hardware used, as it does not conduct experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers, as it does not conduct experiments. |
| Experiment Setup | No | The paper is theoretical and does not provide details about an experimental setup, as it does not conduct experiments. |