A Primer on PAC-Bayesian Learning
arXiv:1901.05353v332.8236 citations
Originality Synthesis-oriented
AI Analysis
This is an incremental work that serves as a primer for researchers and practitioners interested in understanding PAC-Bayesian methods.
The paper provides a self-contained survey on the PAC-Bayesian learning framework, covering its theoretical and algorithmic developments to address generalization in machine learning.
Generalised Bayesian learning algorithms are increasingly popular in machine learning, due to their PAC generalisation properties and flexibility. The present paper aims at providing a self-contained survey on the resulting PAC-Bayes framework and some of its main theoretical and algorithmic developments.