Live briefing
Everything about Bayesian optimization
Sequential model-based optimization of expensive black-box functions
Bayesian optimization is a sequential model-based strategy for global optimization of black-box objective functions whose evaluations are costly. It is commonly used when a single observation requires an experiment, engineering computation, numerical simulation, or machine-learning run, and when derivatives are unavailable or unreliable. The objective need not have a closed-form expression. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Bayesian optimizationA 5-minute orientation
- Skim a recent paper: Bayesian optimization algorithms for accelerator physicsInfluential work since 2022
- Pick up a classic book: Machine LearningThe most-reprinted book on the subject
- Read the landmark paper: Taking the Human Out of the Loop: A Review of Bayesian OptimizationCited 5,607 times
- Go sideways: Probabilistic numericsA closely related topic
- Go sideways: Stochastic gradient Langevin dynamicsA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🏆 Landmark papers (most cited)
Taking the Human Out of the Loop: A Review of Bayesian Optimization
Recent Advances in Bayesian Optimization
Bayesian optimization of time perception
Bayesian optimization of nanoporous materials
Bayesian optimization package: PHYSBO
Bayesian methods in global optimization
Bayesian Optimization for Chemical Reactions
Exploring Bayesian Optimization
🆕 Influential since 2022
📚 Books worth reading
Machine LearningSergios Theodoridis · 2015- 📕Large-Scale Inverse Problems and Quantification of UncertaintyLorenz Biegler, George Biros · 2010
A set of examples of global and discrete optimizationJonas Mockus · 2000
Information pooling and group decision makingUniversity of California, Irvine, Conference on Political Economy (2nd 1983) · 1983
Bayesian heuristic approach to discrete and global optimizationJonas Mockus, J. Mockus · 1996
Bayesian approach to global optimizationJonas Mockus · 1989- 📕Sequentielle lineare Bayes-Schätzer bei quadratischer Schadensfunktion und proportionalen StichprobenkostenJürgen P. Sommer · 1977
Sequentielle Bayes-Verfahren bei messender PrüfungMichael Semmler · 1988
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Probabilistic numerics Probabilistic numerics is an active field of study at the intersection of applied mathematics, statistics, and machine learning centering on the concept…
- Stochastic gradient Langevin dynamics Stochastic gradient Langevin dynamics (SGLD) is an optimization and sampling technique composed of characteristics from Stochastic gradient descent, a…
- Early stopping In machine learning, early stopping is a form of regularization used to avoid overfitting when training a model with an iterative method, such as gradient…
- Bayesian quadrature Bayesian quadrature is a method for approximating intractable integration problems. It falls within the class of probabilistic numerical methods. Bayesian…
- Computer experiment A computer experiment or simulation experiment is an experiment used to study a computer simulation, also referred to as an in silico system. This area…
- Estimation of distribution algorithm Estimation of distribution algorithms (EDAs), sometimes called probabilistic model-building genetic algorithms (PMBGAs), are stochastic optimization methods…
- Bayesian interpretation of kernel regularization Bayesian interpretation of kernel regularization examines how kernel methods in machine learning can be understood through the lens of Bayesian statistics…
- Yield (metric) In integrated circuit reliability engineering, yield is a metric used to quantify the proportion of manufactured chips that meet the required performance…