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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 151–200 of 221 papers

TitleStatusHype
Bayesian Optimisation of Functions on Graphs—0
Bayesian optimisation of large-scale photonic reservoir computers—0
Bayesian optimisation under uncertain inputs—0
Bayesian Optimisation vs. Input Uncertainty Reduction—0
Bayesian Optimisation with Formal Guarantees—0
Bayesian Optimisation with Gaussian Processes for Premise Selection—0
Bayesian Optimistic Optimisation with Exponentially Decaying Regret—0
Bayesian Optimization for Developmental Robotics with Meta-Learning by Parameters Bounds Reduction—0
Bayesian Optimization in AlphaGo—0
Bayesian Policy Reuse—0
Bayesian Quantile and Expectile Optimisation—0
BayesIMP: Uncertainty Quantification for Causal Data Fusion—0
Beyond Expected Return: Accounting for Policy Reproducibility when Evaluating Reinforcement Learning Algorithms—0
Time-Varying Gaussian Process Bandits with Unknown Prior—0
Black-Box Saliency Map Generation Using Bayesian Optimisation—0
BOiLS: Bayesian Optimisation for Logic Synthesis—0
BOP-Elites, a Bayesian Optimisation algorithm for Quality-Diversity search—0
Cell-Free Data Power Control Via Scalable Multi-Objective Bayesian Optimisation—0
Choice functions based multi-objective Bayesian optimisation—0
Contextual Causal Bayesian Optimisation—0
Cost-aware Multi-objective Bayesian optimisation—0
Counterfactual Explanations for Arbitrary Regression Models—0
Covariance Function Pre-Training with m-Kernels for Accelerated Bayesian Optimisation—0
Delayed Feedback in Kernel Bandits—0
Detecting COVID-19 from Breathing and Coughing Sounds using Deep Neural Networks—0
Developmental Bayesian Optimization of Black-Box with Visual Similarity-Based Transfer Learning—0
Differential Evolution and Bayesian Optimisation for Hyper-Parameter Selection in Mixed-Signal Neuromorphic Circuits Applied to UAV Obstacle Avoidance—0
Dimensionality Reduction Techniques for Global Bayesian Optimisation—0
Efficient acquisition rules for model-based approximate Bayesian computation—0
Fast Model-based Policy Search for Universal Policy Networks—0
Few-shot crack image classification using clip based on bayesian optimization—0
Fingerprint Policy Optimisation for Robust Reinforcement Learning—0
GIBBON: General-purpose Information-Based Bayesian OptimisatioN—0
GLASSES: Relieving The Myopia Of Bayesian Optimisation—0
Graph Agnostic Causal Bayesian Optimisation—0
Graph-enabled Reinforcement Learning for Time Series Forecasting with Adaptive Intelligence—0
Heteroscedastic Bayesian Optimisation for Stochastic Model Predictive Control—0
Heteroscedastic Treed Bayesian Optimisation—0
Hidden Markov Model: Tutorial—0
High Dimensional Bayesian Optimisation and Bandits via Additive Models—0
High-Dimensional Bayesian Optimisation with Large-Scale Constraints -- An Application to Aeroelastic Tailoring—0
Impact of HPO on AutoML Forecasting Ensembles—0
'In-Between' Uncertainty in Bayesian Neural Networks—0
Incorporating Expert Prior in Bayesian Optimisation via Space Warping—0
Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation—0
Information-theoretic Inducing Point Placement for High-throughput Bayesian Optimisation—0
Intrinsic Bayesian Optimisation on Complex Constrained Domain—0
Accelerated Bayesian Optimization throughWeight-Prior Tuning—0
Large Language Models for Human-Machine Collaborative Particle Accelerator Tuning through Natural Language—0
Large Language Models Orchestrating Structured Reasoning Achieve Kaggle Grandmaster Level—0
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