Bayesian Optimisation on Local Optima

Initial Designs for Bayesian Optimisation

Wed, 16 Aug 2023 00:00:00 +0000

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Bayesian optimisation is a powerful optimisation technique for black-box functions and processes with expensive evaluations. It is popular for hyperparameter tuning in machine learning, but has many real-world applications as well.

Test Functions for Bayesian Optimisation

Sun, 16 Jul 2023 00:00:00 +0000

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Alternative Surrogate Models for Bayesian Optimisation

Thu, 15 Jun 2023 00:00:00 +0000

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Acquisition Functions for Bayesian Optimisation

Mon, 01 May 2023 00:00:00 +0000

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Bayesian optimisation is a powerful optimisation technique for black-box functions and processes with expensive evaluations. It is popular for hyperparameter tuning and model selection in machine learning, but has many real-world applications as well. One of the key components of Bayesian optimisation is the acquisition function, which guides the search process by balancing exploration and exploitation of the search space. In this post, we will dive into the role of acquisition functions in Bayesian optimisation and discuss some popular examples.

Kernels for Gaussian Processes

Sun, 16 Apr 2023 00:00:00 +0000

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This post takes an extensive look at kernels and discusses the rationales, utility, and limitations of some popular kernels, focusing primarily on their application in Gaussian processes and Bayesian optimisation. Along with the discussion are implementations of the kernels in base R.

Bayesian Optimisation from Scratch in R

Sat, 01 Apr 2023 00:00:00 +0000

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Bayesian optimisation is a powerful technique for optimising expensive functions or processes. In many applications, such as drug discovery, manufacturing, machine learning, or scientific experimentation, the function or process to be optimised may be time consuming or costly to evaluate. Bayesian optimisation provides a framework for sequential experimentation and for finding optima with as few evaluations as possible.