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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
Misspecification-robust likelihood-free inference in high dimensions—0
Regret Bounds for Noise-Free Kernel-Based Bandits—0
ε-shotgun: ε-greedy Batch Bayesian OptimisationCode0
Black-Box Saliency Map Generation Using Bayesian Optimisation—0
Bayesian Quantile and Expectile Optimisation—0
Hidden Markov Model: Tutorial—0
Ordinal Bayesian Optimisation—0
Max-value Entropy Search for Multi-Objective Bayesian OptimizationCode1
Greed is Good: Exploration and Exploitation Trade-offs in Bayesian OptimisationCode0
Trading Convergence Rate with Computational Budget in High Dimensional Bayesian Optimization—0
Interactive Text Ranking with Bayesian Optimisation: A Case Study on Community QA and SummarisationCode0
Parameter Optimization and Learning in a Spiking Neural Network for UAV Obstacle Avoidance targeting Neuromorphic Processors—0
Achieving Robustness to Aleatoric Uncertainty with Heteroscedastic Bayesian OptimisationCode1
Distributional Bayesian optimisation for variational inference on black-box simulatorsCode0
BoTorch: A Framework for Efficient Monte-Carlo Bayesian OptimizationCode2
Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation—0
Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks—0
Antifragile and Robust Heteroscedastic Bayesian Optimisation—0
Bayesian Optimisation with Gaussian Processes for Premise Selection—0
Cost-aware Multi-objective Bayesian optimisation—0
On the Expressiveness of Approximate Inference in Bayesian Neural NetworksCode0
'In-Between' Uncertainty in Bayesian Neural Networks—0
Sparse Spectrum Gaussian Process for Bayesian Optimization—0
Bayesian Optimisation over Multiple Continuous and Categorical InputsCode0
MEMe: An Accurate Maximum Entropy Method for Efficient Approximations in Large-Scale Machine Learning—0
Fast and Reliable Architecture Selection for Convolutional Neural NetworksCode0
Parallel Gaussian process surrogate Bayesian inference with noisy likelihood evaluationsCode0
Bayesian Search for Robust Optima—0
Effective Estimation of Deep Generative Language ModelsCode0
Meta-Learning surrogate models for sequential decision making—0
Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with DragonflyCode0
Stable Bayesian Optimisation via Direct Stability Quantification—0
Bayesian optimisation under uncertain inputs—0
Gaussian Process Priors for Dynamic Paired Comparison ModellingCode0
On resampling vs. adjusting probabilistic graphical models in estimation of distribution algorithms—0
Multi-objective Bayesian optimisation with preferences over objectives—0
Asynchronous Batch Bayesian Optimisation with Improved Local PenalisationCode0
Fitting A Mixture Distribution to Data: TutorialCode0
Bayesian Optimization in AlphaGo—0
Batch Selection for Parallelisation of Bayesian QuadratureCode0
Algorithmic Assurance: An Active Approach to Algorithmic Testing using Bayesian OptimisationCode0
Efficient Bayesian Experimental Design for Implicit ModelsCode0
Hyperparameter Learning via Distributional TransferCode0
Developmental Bayesian Optimization of Black-Box with Visual Similarity-Based Transfer Learning—0
Bayesian functional optimisation with shape prior—0
Fingerprint Policy Optimisation for Robust Reinforcement Learning—0
Accelerated Bayesian Optimization throughWeight-Prior Tuning—0
Learning to Race through Coordinate Descent Bayesian Optimisation—0
Rapid Bayesian optimisation for synthesis of short polymer fiber materials—0
Covariance Function Pre-Training with m-Kernels for Accelerated Bayesian Optimisation—0
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