Papers Bayesian Optimisation
“Bayesian Optimisation” 태그가 달린 논문 221편 · 필터 해제
Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly
Bayesian Optimisation (BO) refers to a suite of techniques for global optimisation of expensive black box functions, which use introspective Bayesian models of the function to efficiently search for the optimum. While BO…
Bayesian OptimisationStable Bayesian Optimisation via Direct Stability Quantification
In this paper we consider the problem of finding stable maxima of expensive (to evaluate) functions. We are motivated by the optimisation of physical and industrial processes where, for some input ranges, small and unavo…
Bayesian OptimisationBayesian optimisation under uncertain inputs
Bayesian optimisation (BO) has been a successful approach to optimise functions which are expensive to evaluate and whose observations are noisy. Classical BO algorithms, however, do not account for errors about the loca…
Bayesian OptimisationGaussian Process Priors for Dynamic Paired Comparison Modelling
Dynamic paired comparison models, such as Elo and Glicko, are frequently used for sports prediction and ranking players or teams. We present an alternative dynamic paired comparison model which uses a Gaussian Process (G…
Bayesian InferenceBayesian OptimisationOn resampling vs. adjusting probabilistic graphical models in estimation of distribution algorithms
The Bayesian Optimisation Algorithm (BOA) is an Estimation of Distribution Algorithm (EDA) that uses a Bayesian network as probabilistic graphical model (PGM). Determining the optimal Bayesian network structure given a s…
Bayesian OptimisationMulti-objective Bayesian optimisation with preferences over objectives
We present a multi-objective Bayesian optimisation algorithm that allows the user to express preference-order constraints on the objectives of the type "objective A is more important than objective B". These preferences …
Bayesian OptimisationAsynchronous Batch Bayesian Optimisation with Improved Local Penalisation
Batch Bayesian optimisation (BO) has been successfully applied to hyperparameter tuning using parallel computing, but it is wasteful of resources: workers that complete jobs ahead of others are left idle. We address this…
Bayesian OptimisationFitting A Mixture Distribution to Data: Tutorial
This paper is a step-by-step tutorial for fitting a mixture distribution to data. It merely assumes the reader has the background of calculus and linear algebra. Other required background is briefly reviewed before expla…
Bayesian InferenceBayesian OptimisationClusteringBayesian Optimization in AlphaGo
During the development of AlphaGo, its many hyper-parameters were tuned with Bayesian optimization multiple times. This automatic tuning process resulted in substantial improvements in playing strength. For example, prio…
Bayesian OptimisationBayesian OptimizationBatch Selection for Parallelisation of Bayesian Quadrature
Integration over non-negative integrands is a central problem in machine learning (e.g. for model averaging, (hyper-)parameter marginalisation, and computing posterior predictive distributions). Bayesian Quadrature is a …
Bayesian OptimisationBIG-bench Machine LearningNumerical IntegrationAlgorithmic Assurance: An Active Approach to Algorithmic Testing using Bayesian Optimisation
We introduce algorithmic assurance, the problem of testing whether machine learning algorithms are conforming to their intended design goal. We address this problem by proposing an efficient framework for algorithmic tes…
Active LearningBayesian OptimisationEfficient Bayesian Experimental Design for Implicit Models
Bayesian experimental design involves the optimal allocation of resources in an experiment, with the aim of optimising cost and performance. For implicit models, where the likelihood is intractable but sampling from the …
Bayesian OptimisationExperimental DesignHyperparameter Learning via Distributional Transfer
Bayesian optimisation is a popular technique for hyperparameter learning but typically requires initial exploration even in cases where similar prior tasks have been solved. We propose to transfer information across task…
Bayesian OptimisationDevelopmental Bayesian Optimization of Black-Box with Visual Similarity-Based Transfer Learning
We present a developmental framework based on a long-term memory and reasoning mechanisms (Vision Similarity and Bayesian Optimisation). This architecture allows a robot to optimize autonomously hyper-parameters that nee…
Bayesian OptimisationBayesian OptimizationTransfer LearningBayesian functional optimisation with shape prior
Real world experiments are expensive, and thus it is important to reach a target in minimum number of experiments. Experimental processes often involve control variables that changes over time. Such problems can be formu…
Bayesian OptimisationFingerprint Policy Optimisation for Robust Reinforcement Learning
Policy gradient methods ignore the potential value of adjusting environment variables: unobservable state features that are randomly determined by the environment in a physical setting, but are controllable in a simulato…
Bayesian OptimisationContinuous ControlPolicy Gradient Methodsreinforcement-learning+2Accelerated Bayesian Optimization throughWeight-Prior Tuning
Bayesian optimization (BO) is a widely-used method for optimizing expensive (to evaluate) problems. At the core of most BO methods is the modeling of the objective function using a Gaussian Process (GP) whose covariance …
Bayesian OptimisationBayesian OptimizationTransfer LearningLearning to Race through Coordinate Descent Bayesian Optimisation
In the automation of many kinds of processes, the observable outcome can often be described as the combined effect of an entire sequence of actions, or controls, applied throughout its execution. In these cases, strategi…
Bayesian OptimisationCar RacingvalidRapid Bayesian optimisation for synthesis of short polymer fiber materials
The discovery of processes for the synthesis of new materials involves many decisions about process design, operation, and material properties. Experimentation is crucial but as complexity increases, exploration of varia…
Bayesian OptimisationBIG-bench Machine LearningCovariance Function Pre-Training with m-Kernels for Accelerated Bayesian Optimisation
The paper presents a novel approach to direct covariance function learning for Bayesian optimisation, with particular emphasis on experimental design problems where an existing corpus of condensed knowledge is present. T…
Bayesian OptimisationExperimental Design