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Bayesian Optimisation

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

Papers

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Showing 51–100 of 221 papers

TitleStatusHype
On the development of a practical Bayesian optimisation algorithm for expensive experiments and simulations with changing environmental conditionsCode0
Time-Varying Gaussian Process Bandits with Unknown Prior—0
Automated Machine Learning for Positive-Unlabelled LearningCode0
Long-run Behaviour of Multi-fidelity Bayesian Optimisation—0
High-Dimensional Bayesian Optimisation with Large-Scale Constraints -- An Application to Aeroelastic Tailoring—0
Beyond Expected Return: Accounting for Policy Reproducibility when Evaluating Reinforcement Learning Algorithms—0
Search Strategies for Self-driving Laboratories with Pending Experiments—0
Expert-guided Bayesian Optimisation for Human-in-the-loop Experimental Design of Known SystemsCode0
Data-driven Prior Learning for Bayesian OptimisationCode0
Impact of HPO on AutoML Forecasting Ensembles—0
Multi-fidelity Bayesian Optimisation of Syngas Fermentation Simulators—0
Robust and Conjugate Gaussian Process RegressionCode0
Shaping of Magnetic Field Coils in Fusion Reactors using Bayesian Optimisation—0
Graph-enabled Reinforcement Learning for Time Series Forecasting with Adaptive Intelligence—0
Optimal Observation-Intervention Trade-Off in Optimisation Problems with Causal Structure—0
Will More Expressive Graph Neural Networks do Better on Generative Tasks?—0
Machine Learning-Assisted Discovery of Flow Reactor Designs—0
Adaptive Batch Sizes for Active Learning A Probabilistic Numerics ApproachCode0
Bayesian Optimisation of Functions on Graphs—0
Bayesian Optimisation Against Climate Change: Applications and BenchmarksCode0
End-to-End Meta-Bayesian Optimisation with Transformer Neural ProcessesCode0
Multi-objective optimisation via the R2 utilitiesCode0
Uncovering Energy-Efficient Practices in Deep Learning Training: Preliminary Steps Towards Green AI—0
Protein Sequence Design with Batch Bayesian OptimisationCode0
Automated control and optimisation of laser driven ion acceleration—0
MONGOOSE: Path-wise Smooth Bayesian Optimisation via Meta-learning—0
Detection and classification of vocal productions in large scale audio recordingsCode0
Delayed Feedback in Kernel Bandits—0
Are Random Decompositions all we need in High Dimensional Bayesian Optimisation?Code0
Intrinsic Bayesian Optimisation on Complex Constrained Domain—0
Contextual Causal Bayesian Optimisation—0
Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation—0
Cell-Free Data Power Control Via Scalable Multi-Objective Bayesian Optimisation—0
Policy learning for many outcomes of interest: Combining optimal policy trees with multi-objective Bayesian optimisationCode0
Batch Bayesian optimisation via density-ratio estimation with guaranteesCode0
Batch Bayesian Optimization via Particle Gradient FlowsCode0
Bayesian learning of feature spaces for multitasks problems—0
The case for fully Bayesian optimisation in small-sample trialsCode0
Nonstationary Continuum-Armed Bandit Strategies for Automated Trading in a Simulated Financial MarketCode0
Investigating Bayesian optimization for expensive-to-evaluate black box functions: Application in fluid dynamicsCode0
A Two-Stage Bayesian Optimisation for Automatic Tuning of an Unscented Kalman Filter for Vehicle Sideslip Angle Estimation—0
A penalisation method for batch multi-objective Bayesian optimisation with application in heat exchanger designCode0
Information-theoretic Inducing Point Placement for High-throughput Bayesian Optimisation—0
Sample-Efficient Optimisation with Probabilistic Transformer Surrogates—0
Bayesian learning of effective chemical master equations in crowded intracellular conditionsCode0
Mono-surrogate vs Multi-surrogate in Multi-objective Bayesian Optimisation—0
R-MBO: A Multi-surrogate Approach for Preference Incorporation in Multi-objective Bayesian Optimisation—0
Wind Farm Layout Optimisation using Set Based Multi-objective Bayesian Optimisation—0
MBORE: Multi-objective Bayesian Optimisation by Density-Ratio EstimationCode0
Adaptive Model Predictive Control by Learning Classifiers—0
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