A Primer on PAC-Bayesian Learning

01/16/2019
by   Benjamin Guedj, et al.
0

Generalized Bayesian learning algorithms are increasingly popular in machine learning, due to their PAC generalization 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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
08/20/2019

Fast-rate PAC-Bayes Generalization Bounds via Shifted Rademacher Processes

The developments of Rademacher complexity and PAC-Bayesian theory have b...
research
12/07/2020

A PAC-Bayesian Perspective on Structured Prediction with Implicit Loss Embeddings

Many practical machine learning tasks can be framed as Structured predic...
research
11/12/2013

A PAC-Bayesian bound for Lifelong Learning

Transfer learning has received a lot of attention in the machine learnin...
research
05/15/2023

SAT-Based PAC Learning of Description Logic Concepts

We propose bounded fitting as a scheme for learning description logic co...
research
08/20/2018

PAC-learning is Undecidable

The problem of attempting to learn the mapping between data and labels i...
research
09/08/2023

Generalization Bounds: Perspectives from Information Theory and PAC-Bayes

A fundamental question in theoretical machine learning is generalization...
research
04/28/2021

Self-Bounding Majority Vote Learning Algorithms by the Direct Minimization of a Tight PAC-Bayesian C-Bound

In the PAC-Bayesian literature, the C-Bound refers to an insightful rela...

Please sign up or login with your details

Forgot password? Click here to reset