PAC-Bayes Learning Bounds for Sample-Dependent Priors
Authors: Pranjal Awasthi, Satyen Kale, Stefani Karp, Mehryar Mohri
NeurIPS 2020 | Venue 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 EMAIL Satyen Kale Google Research EMAIL Stefani Karp Google Research and Carnegie Mellon University EMAIL Mehryar Mohri Google Research and Courant Institute of Mathematical Sciences 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. |
| 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. |