Sequential methods | Sequential experiments | Stochastic optimization
Bayesian optimization is a sequential design strategy for global optimization of black-box functions that does not assume any functional forms. It is usually employed to optimize expensive-to-evaluate functions. (Wikipedia).
(ML 7.1) Bayesian inference - A simple example
Illustration of the main idea of Bayesian inference, in the simple case of a univariate Gaussian with a Gaussian prior on the mean (and known variances).
From playlist Machine Learning
(ML 11.8) Bayesian decision theory
Choosing an optimal decision rule under a Bayesian model. An informal discussion of Bayes rules, generalized Bayes rules, and the complete class theorems.
From playlist Machine Learning
Comparing Bayesian optimization with traditional sampling
Welcome to video #2 of the Adaptive Experimentation series, presented by graduate student Sterling Baird @sterling-baird at the 18th IEEE Conference on eScience in Salt Lake City, UT (Oct 10-14, 2022). In this video Sterling introduces Bayesian Optimization as an alternative method for sa
From playlist Optimization tutorial
Tim Sullivan: Brittleness and robustness of Bayesian inference for complex systems
Find this video and other talks given by worldwide mathematicians on CIRM's Audiovisual Mathematics Library: http://library.cirm-math.fr. And discover all its functionalities: - Chapter markers and keywords to watch the parts of your choice in the video - Videos enriched with abstracts, b
From playlist Numerical Analysis and Scientific Computing
Marcelo Pereyra: Bayesian inference and mathematical imaging - Lecture 1: Bayesian analysis...
Bayesian inference and mathematical imaging - Part 1: Bayesian analysis and decision theory Abstract: This course presents an overview of modern Bayesian strategies for solving imaging inverse problems. We will start by introducing the Bayesian statistical decision theory framework underp
From playlist Probability and Statistics
(ML 7.2) Aspects of Bayesian inference
An informal overview of Bayesian inference, Bayesian procedures, Objective versus Subjective Bayes, Pros/Cons of a Bayesian approach, and priors.
From playlist Machine Learning
From playlist COMP0168 (2020/21)
Grey-box Bayesian Optimization by Peter Frazier
A Google TechTalk, presented by Peter I. Frazier, 2021/06/08 ABSTRACT: Bayesian optimization is a powerful tool for optimizing time-consuming-to-evaluate non-convex derivative-free objective functions. While BayesOpt has historically been deployed as a black-box optimizer, recent advances
From playlist Google BayesOpt Speaker Series 2021-2022
A description of the syllabus that will be covered in this course on Bayesian statistics. If you are interested in seeing more of the material, arranged into a playlist, please visit: https://www.youtube.com/playlist?list=PLFDbGp5YzjqXQ4oE4w9GVWdiokWB9gEpm Unfortunately, Ox Educ is no m
From playlist Bayesian statistics: a comprehensive course
Efficient Exploration in Bayesian Optimization – Optimism and Beyond by Andreas Krause
A Google TechTalk, presented by Andreas Krause, 2021/06/07 ABSTRACT: A central challenge in Bayesian Optimization and related tasks is the exploration—exploitation dilemma: Selecting inputs that are informative about the unknown function, while focusing exploration where we expect high ret
From playlist Google BayesOpt Speaker Series 2021-2022
PB2 - Population-Based Bandit Optimization
Notion Link: https://ebony-scissor-725.notion.site/Henry-AI-Labs-Weekly-Update-July-15th-2021-a68f599395e3428c878dc74c5f0e1124 Chapters 0:00 Introduction 2:41 Hyperparameter Optimization 3:44 Population-Based Training 6:12 Evolution + Bayesian Optimization 8:54 ASHA 10:48 Results Thanks
From playlist AI Weekly Update - July 15th, 2021!
Scalable hyperparameter transfer learning - Perrone - Workshop 3 - CEB T1 2019
Valerio Perrone (Amazon) / 01.04.2019 Scalable hyperparameter transfer learning. Bayesian optimization (BO) is a model-based approach for gradient-free black-box function optimization, such as hyperparameter optimization. Typically, BO relies on conventional Gaussian process (GP) regres
From playlist 2019 - T1 - The Mathematics of Imaging
Supercharging Decision Making with Bayes
Bayesian Decision Theory is a fundamental statistical approach to the problem of pattern classification. It is considered as the ideal pattern classifier and often used as the benchmark for other algorithms because its decision rule automatically minimizes its loss function. PUBLICATION P
From playlist Machine Learning
Bayesian optimisation for likelihood-free cosmological (...) - Leclercq - Workshop 2 - CEB T3 2018
Leclercq (Imperial College) / 22.10.2018 Bayesian optimisation for likelihood-free cosmological inference ---------------------------------- Vous pouvez nous rejoindre sur les réseaux sociaux pour suivre nos actualités. Facebook : https://www.facebook.com/InstitutHenriPoincare/ Twitter
From playlist 2018 - T3 - Analytics, Inference, and Computation in Cosmology
Stanford CS330 Deep Multi-Task & Meta Learning - Bayesian Meta-Learning l 2022 I Lecture 12
For more information about Stanford's Artificial Intelligence programs visit: https://stanford.io/ai To follow along with the course, visit: https://cs330.stanford.edu/ To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu Chelsea Finn Computer
From playlist Stanford CS330: Deep Multi-Task and Meta Learning I Autumn 2022
Probability theory and AI | The Royal Society
Join Professor Zoubin Ghahramani to explore the foundations of probabilistic AI and how it relates to deep learning. 🔔Subscribe to our channel for exciting science videos and live events, many hosted by Brian Cox, our Professor for Public Engagement: https://bit.ly/3fQIFXB #Probability #A
From playlist Latest talks and lectures
Bayesian Optimization in the Wild: Risk-Averse Decisions and Budget Constraints
A Google TechTalk, presented by Anastasia Makarova, 2022/08/23 Google BayesOpt Speaker Series - ABSTRACT: Black-box optimization tasks frequently arise in high-stakes applications such as material discovery or hyperparameter tuning of complex systems. In many of these applications, there i
From playlist Google BayesOpt Speaker Series 2021-2022