Papers Bayesian Optimisation
“Bayesian Optimisation” 태그가 달린 논문 221편 · 필터 해제
Choice functions based multi-objective Bayesian optimisation
In this work we introduce a new framework for multi-objective Bayesian optimisation where the multi-objective functions can only be accessed via choice judgements, such as ``I pick options A,B,C among this set of five op…
Bayesian OptimisationApproximate Bayesian Optimisation for Neural Networks
A body of work has been done to automate machine learning algorithm to highlight the importance of model choice. Automating the process of choosing the best forecasting model and its corresponding parameters can result t…
Bayesian OptimisationDensity Ratio EstimationGaussian ProcessesBayesian Optimisation for Sequential Experimental Design with Applications in Additive Manufacturing
Bayesian optimization (BO) is an approach to globally optimizing black-box objective functions that are expensive to evaluate. BO-powered experimental design has found wide application in materials science, chemistry, ex…
Bayesian OptimisationBayesian OptimizationExperimental DesignCounterfactual Explanations for Arbitrary Regression Models
We present a new method for counterfactual explanations (CFEs) based on Bayesian optimisation that applies to both classification and regression models. Our method is a globally convergent search algorithm with support f…
Bayesian OptimisationcounterfactualregressionAttacking Graph Classification via Bayesian Optimisation
Graph neural networks have been shown to be vulnerable to adversarial attacks. While the majority of the literature focuses on such vulnerability in node-level classification tasks, little effort has been dedicated to at…
Adversarial RobustnessBayesian OptimisationClassificationGraph ClassificationBayesian Optimisation with Formal Guarantees
Application domains of Bayesian optimization include optimizing black-box functions or very complex functions. The functions we are interested in describe complex real-world systems applied in industrial settings. Even t…
Bayesian OptimisationBayesian OptimizationNeuroadaptive electroencephalography: a proof-of-principle study in infants
A core goal of functional neuroimaging is to study how the environment is processed in the brain. The mainstream paradigm involves concurrently measuring a broad spectrum of brain responses to a small set of environmenta…
Bayesian OptimisationEEGElectroencephalogram (EEG)EXPObench: Benchmarking Surrogate-based Optimisation Algorithms on Expensive Black-box Functions
Surrogate algorithms such as Bayesian optimisation are especially designed for black-box optimisation problems with expensive objectives, such as hyperparameter tuning or simulation-based optimisation. In the literature,…
Bayesian OptimisationBenchmarkingHigh-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning
We introduce a method combining variational autoencoders (VAEs) and deep metric learning to perform Bayesian optimisation (BO) over high-dimensional and structured input spaces. By adapting ideas from deep metric learnin…
Bayesian OptimisationMetric LearningMolecular Graph GenerationVocal Bursts Intensity PredictionBayesIMP: Uncertainty Quantification for Causal Data Fusion
While causal models are becoming one of the mainstays of machine learning, the problem of uncertainty quantification in causal inference remains challenging. In this paper, we study the causal data fusion problem, where …
Bayesian OptimisationCausal InferenceUncertainty QuantificationBayesian Optimisation for Constrained Problems
Many real-world optimisation problems such as hyperparameter tuning in machine learning or simulation-based optimisation can be formulated as expensive-to-evaluate black-box functions. A popular approach to tackle such p…
Bayesian OptimisationBenchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains
In the field of machine learning (ML) for materials optimization, active learning algorithms, such as Bayesian Optimization (BO), have been leveraged for guiding autonomous and high-throughput experimentation systems. Ho…
Active LearningBayesian OptimisationBayesian OptimizationBenchmarking+3AutoLRS: Automatic Learning-Rate Schedule by Bayesian Optimization on the Fly
The learning rate (LR) schedule is one of the most important hyper-parameters needing careful tuning in training DNNs. However, it is also one of the least automated parts of machine learning systems and usually costs si…
Bayesian OptimisationBayesian OptimizationImage ClassificationMachine Translation+1Bayesian Optimistic Optimisation with Exponentially Decaying Regret
Bayesian optimisation (BO) is a well-known efficient algorithm for finding the global optimum of expensive, black-box functions. The current practical BO algorithms have regret bounds ranging from $\mathcal{O}(\frac{logN…
Bayesian OptimisationHow Bayesian Should Bayesian Optimisation Be?
Bayesian optimisation (BO) uses probabilistic surrogate models - usually Gaussian processes (GPs) - for the optimisation of expensive black-box functions. At each BO iteration, the GP hyperparameters are fit to previousl…
Bayesian OptimisationGaussian ProcessesOCTIS: Comparing and Optimizing Topic models is Simple!
In this paper, we present OCTIS, a framework for training, analyzing, and comparing Topic Models, whose optimal hyper-parameters are estimated using a Bayesian Optimization approach. The proposed solution integrates seve…
Bayesian OptimisationBayesian OptimizationTopic ModelsBayesian Optimisation for a Biologically Inspired Population Neural Network
We have used Bayesian Optimisation (BO) to find hyper-parameters in an existing biologically plausible population neural network. The 8-dimensional optimal hyper-parameter combination should be such that the network dyna…
Bayesian OptimisationRhythmTime SeriesTime Series AnalysisWhat Makes an Effective Scalarising Function for Multi-Objective Bayesian Optimisation?
Performing multi-objective Bayesian optimisation by scalarising the objectives avoids the computation of expensive multi-dimensional integral-based acquisition functions, instead of allowing one-dimensional standard acqu…
Bayesian OptimisationApproximate Bayesian inference from noisy likelihoods with Gaussian process emulated MCMC
We present a framework for approximate Bayesian inference when only a limited number of noisy log-likelihood evaluations can be obtained due to computational constraints, which is becoming increasingly common for applica…
Bayesian InferenceBayesian OptimisationExperimental DesignThink Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces
High-dimensional black-box optimisation remains an important yet notoriously challenging problem. Despite the success of Bayesian optimisation methods on continuous domains, domains that are categorical, or that mix cont…
Bayesian Optimisation