Bayesian Optimisation
87 papers with code • 0 benchmarks • 0 datasets
Expensive black-box functions are a common problem in many disciplines, including tuning the parameters of machine learning algorithms, robotics, and other engineering design problems. Bayesian Optimisation is a principled and efficient technique for the global optimisation of these functions. The idea behind Bayesian Optimisation is to place a prior distribution over the target function and then update that prior with a set of “true” observations of the target function by expensively evaluating it in order to produce a posterior predictive distribution. The posterior then informs where to make the next observation of the target function through the use of an acquisition function, which balances the exploitation of regions known to have good performance with the exploration of regions where there is little information about the function’s response.
Source: A Bayesian Approach for the Robust Optimisation of Expensive-to-Evaluate Functions
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Libraries
Use these libraries to find Bayesian Optimisation models and implementationsLatest papers
End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes
We enable this end-to-end framework with reinforcement learning (RL) to tackle the lack of labelled acquisition data.
Multi-objective optimisation via the R2 utilities
As part of our work, we show that these utilities are monotone and submodular set functions which can be optimised effectively using greedy optimisation algorithms.
NUBO: A Transparent Python Package for Bayesian Optimisation
NUBO, short for Newcastle University Bayesian Optimisation, is a Bayesian optimisation framework for the optimisation of expensive-to-evaluate black-box functions, such as physical experiments and computer simulators.
Applications of Gaussian Processes at Extreme Lengthscales: From Molecules to Black Holes
GPs can make predictions with consideration of uncertainty, for example in the virtual screening of molecules and materials, and can also make inferences about incomplete data such as the latent emission signature from a black hole accretion disc.
Protein Sequence Design with Batch Bayesian Optimisation
Protein sequence design is a challenging problem in protein engineering, which aims to discover novel proteins with useful biological functions.
Detection and classification of vocal productions in large scale audio recordings
The pipeline trains a model on 72 and 77 minutes of labeled audio recordings, with an accuracy of 94. 58% and 99. 76%.
Are Random Decompositions all we need in High Dimensional Bayesian Optimisation?
Learning decompositions of expensive-to-evaluate black-box functions promises to scale Bayesian optimisation (BO) to high-dimensional problems.
AutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-Tuning
Large pretrained language models are widely used in downstream NLP tasks via task-specific fine-tuning, but such procedures can be costly.
SOBER: Highly Parallel Bayesian Optimization and Bayesian Quadrature over Discrete and Mixed Spaces
Batch Bayesian optimisation and Bayesian quadrature have been shown to be sample-efficient methods of performing optimisation and quadrature where expensive-to-evaluate objective functions can be queried in parallel.
Policy learning for many outcomes of interest: Combining optimal policy trees with multi-objective Bayesian optimisation
Methods for learning optimal policies use causal machine learning models to create human-interpretable rules for making choices around the allocation of different policy interventions.