Pac Bayes

Media Summary: Speakers: Andrew Foong, David Burt, Javier Antoran Abstract: The goal of machine learning algorithms is to produce predictors having the smallest possible risk (expected loss). Since the ... NIPS 2016 spotlight Poster (Mon Dec 5th) Manuscript: Slides: ...

Pac Bayes - Detailed Analysis & Overview

Speakers: Andrew Foong, David Burt, Javier Antoran Abstract: The goal of machine learning algorithms is to produce predictors having the smallest possible risk (expected loss). Since the ... NIPS 2016 spotlight Poster (Mon Dec 5th) Manuscript: Slides: ... Gintare Karolina Dziugaite (Element AI) Frontiers of Deep Learning. Workshop on Theory of Deep Learning: Where next? Topic: In this video, I give a short introduction into our current research paper "

Talk by Pascal Germain at NIPS 2012 Workshop Multi-trade-off in Machine Learning.

Photo Gallery

The PAC-Bayes Guarantee

An Introduction to PAC-Bayes

Pierre Alquier (ESSEC) - PAC Bayes: introduction and overview

PAC-Bayesian Machine Learning: Learning by Optimizing a Performance Guarantee

Part 1: generalization and PAC bayesian learning

A (condensed) primer on PAC-Bayesian learning, followed by News from the PAC-Bayes frontline

[ML/DL] PAC-Bayesian Bound for Deep Learning Models

NIPS 2016 spotlight - PAC Bayesian Theory Meets Bayesian Inference

Studying Generalization in Deep Learning via PAC-Bayes

PAC-Bayes control for obstacle avoidance

PAC-Bayesian approaches to understanding generalization in deep learning - Gintare Dziugaite

AISTATS 2023: PAC-Bayesian Learning of Optimization Algorithms

View Detailed Profile

The PAC-Bayes Guarantee

The PAC-Bayes Guarantee

... is the

An Introduction to PAC-Bayes

An Introduction to PAC-Bayes

Speakers: Andrew Foong, David Burt, Javier Antoran Abstract:

Pierre Alquier (ESSEC) - PAC Bayes: introduction and overview

Pierre Alquier (ESSEC) - PAC Bayes: introduction and overview

Abstract: The

PAC-Bayesian Machine Learning: Learning by Optimizing a Performance Guarantee

PAC-Bayesian Machine Learning: Learning by Optimizing a Performance Guarantee

The goal of machine learning algorithms is to produce predictors having the smallest possible risk (expected loss). Since the ...

Part 1: generalization and PAC bayesian learning

Part 1: generalization and PAC bayesian learning

So

A (condensed) primer on PAC-Bayesian learning, followed by News from the PAC-Bayes frontline

A (condensed) primer on PAC-Bayesian learning, followed by News from the PAC-Bayes frontline

A (condensed) primer on

[ML/DL] PAC-Bayesian Bound for Deep Learning Models

[ML/DL] PAC-Bayesian Bound for Deep Learning Models

In this video, we discuss the

NIPS 2016 spotlight - PAC Bayesian Theory Meets Bayesian Inference

NIPS 2016 spotlight - PAC Bayesian Theory Meets Bayesian Inference

NIPS 2016 spotlight Poster #29 (Mon Dec 5th) Manuscript: https://arxiv.org/abs/1605.08636 Slides: ...

Studying Generalization in Deep Learning via PAC-Bayes

Studying Generalization in Deep Learning via PAC-Bayes

Gintare Karolina Dziugaite (Element AI) https://simons.berkeley.edu/talks/tbd-77 Frontiers of Deep Learning.

PAC-Bayes control for obstacle avoidance

PAC-Bayes control for obstacle avoidance

Results from: "

PAC-Bayesian approaches to understanding generalization in deep learning - Gintare Dziugaite

PAC-Bayesian approaches to understanding generalization in deep learning - Gintare Dziugaite

Workshop on Theory of Deep Learning: Where next? Topic:

AISTATS 2023: PAC-Bayesian Learning of Optimization Algorithms

AISTATS 2023: PAC-Bayesian Learning of Optimization Algorithms

In this video, I give a short introduction into our current research paper "

PAC Bayesian Learning and Domain Adaptation

PAC Bayesian Learning and Domain Adaptation

Talk by Pascal Germain at NIPS 2012 Workshop Multi-trade-off in Machine Learning.