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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 1–50 of 221 papers

TitleStatusHype
Modelling the Effects of Hearing Loss on Neural Coding in the Auditory Midbrain with Variational Conditioning—0
Soft Reasoning: Navigating Solution Spaces in Large Language Models through Controlled Embedding Exploration—0
Machine Learning-based Regional Cooling Demand Prediction with Optimised Dataset Partitioning—0
Few-shot crack image classification using clip based on bayesian optimization—0
Nested Expectations with Kernel QuadratureCode0
Mean-Field Bayesian OptimisationCode0
Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning—0
Multi-view Bayesian optimisation in reduced dimension for engineering design—0
Dimensionality Reduction Techniques for Global Bayesian Optimisation—0
Nonmyopic Global Optimisation via Approximate Dynamic ProgrammingCode0
Large Language Models Orchestrating Structured Reasoning Achieve Kaggle Grandmaster Level—0
Graph Agnostic Causal Bayesian Optimisation—0
Residual Deep Gaussian Processes on ManifoldsCode0
Sample-efficient Bayesian Optimisation Using Known InvariancesCode0
Spectral Representations for Accurate Causal Uncertainty Quantification with Gaussian ProcessesCode0
Principled Bayesian Optimisation in Collaboration with Human ExpertsCode0
Bayesian Optimisation with Unknown Hyperparameters: Regret Bounds Logarithmically Closer to OptimalCode0
Large Language Models for Human-Machine Collaborative Particle Accelerator Tuning through Natural Language—0
Unsupervised machine learning for data-driven rock mass classification: addressing limitations in existing systems using drilling data—0
A Quadrature Approach for General-Purpose Batch Bayesian Optimization via Probabilistic LiftingCode1
Some variation of COBRA in sequential learning setup—0
Personalized LLM Response Generation with Parameterized Memory InjectionCode0
Single and Multi-Objective Real-Time Optimisation of an Industrial Injection Moulding Process via a Bayesian Adaptive Design of Experiment Approach—0
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
Cheetah: Bridging the Gap Between Machine Learning and Particle Accelerator Physics with High-Speed, Differentiable SimulationsCode2
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
Stochastic Gradient Descent for Gaussian Processes Done RightCode1
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
Learning to Do or Learning While Doing: Reinforcement Learning and Bayesian Optimisation for Online Continuous TuningCode1
End-to-End Meta-Bayesian Optimisation with Transformer Neural ProcessesCode0
Multi-objective optimisation via the R2 utilitiesCode0
NUBO: A Transparent Python Package for Bayesian OptimizationCode1
Applications of Gaussian Processes at Extreme Lengthscales: From Molecules to Black HolesCode1
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