Papers Bayesian Optimisation
“Bayesian Optimisation” 태그가 달린 논문 221편 · 필터 해제
SOBER: Highly Parallel Bayesian Optimization and Bayesian Quadrature over Discrete and Mixed Spaces
Batch Bayesian optimisation and Bayesian quadrature have been shown to be sample-efficient methods of performing optimisation and quadrature where expensive-to-evaluate objective functions can be queried in parallel. How…
Bayesian OptimisationBayesian OptimizationDrug DiscoveryInducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation
Sparse Gaussian Processes are a key component of high-throughput Bayesian Optimisation (BO) loops; however, we show that existing methods for allocating their inducing points severely hamper optimisation performance. By …
Bayesian OptimisationDecision MakingDiversityGaussian Processes+2Cell-Free Data Power Control Via Scalable Multi-Objective Bayesian Optimisation
Cell-free multi-user multiple input multiple output networks are a promising alternative to classical cellular architectures, since they have the potential to provide uniform service quality and high resource utilisation…
Bayesian OptimisationManagementPolicy learning for many outcomes of interest: Combining optimal policy trees with multi-objective Bayesian optimisation
Methods for learning optimal policies use causal machine learning models to create human-interpretable rules for making choices around the allocation of different policy interventions. However, in realistic policy-making…
Bayesian OptimisationGAUCHE: A Library for Gaussian Processes in Chemistry
We introduce GAUCHE, a library for GAUssian processes in CHEmistry. Gaussian processes have long been a cornerstone of probabilistic machine learning, affording particular advantages for uncertainty quantification and Ba…
Bayesian OptimisationGaussian ProcessesUncertainty QuantificationBatch Bayesian optimisation via density-ratio estimation with guarantees
Bayesian optimisation (BO) algorithms have shown remarkable success in applications involving expensive black-box functions. Traditionally BO has been set as a sequential decision-making process which estimates the utili…
Bayesian InferenceBayesian OptimisationDecision MakingDensity Ratio Estimation+1Batch Bayesian Optimization via Particle Gradient Flows
Bayesian Optimisation (BO) methods seek to find global optima of objective functions which are only available as a black-box or are expensive to evaluate. Such methods construct a surrogate model for the objective functi…
Bayesian InferenceBayesian OptimisationBayesian OptimizationBayesian learning of feature spaces for multitasks problems
This paper introduces a novel approach for multi-task regression that connects Kernel Machines (KMs) and Extreme Learning Machines (ELMs) through the exploitation of the Random Fourier Features (RFFs) approximation of th…
Bayesian OptimisationregressionThe case for fully Bayesian optimisation in small-sample trials
While sample efficiency is the main motive for use of Bayesian optimisation when black-box functions are expensive to evaluate, the standard approach based on type II maximum likelihood (ML-II) may fail and result in dis…
Bayesian OptimisationNonstationary Continuum-Armed Bandit Strategies for Automated Trading in a Simulated Financial Market
We approach the problem of designing an automated trading strategy that can consistently profit by adapting to changing market conditions. This challenge can be framed as a Nonstationary Continuum-Armed Bandit (NCAB) pro…
Bayesian OptimisationBayesian OptimizationMulti-Armed BanditsDeveloping Optimal Causal Cyber-Defence Agents via Cyber Security Simulation
In this paper we explore cyber security defence, through the unification of a novel cyber security simulator with models for (causal) decision-making through optimisation. Particular attention is paid to a recently publi…
Bayesian OptimisationDecision MakingInvestigating Bayesian optimization for expensive-to-evaluate black box functions: Application in fluid dynamics
Bayesian optimization provides an effective method to optimize expensive-to-evaluate black box functions. It has been widely applied to problems in many fields, including notably in computer science, e.g. in machine lear…
Bayesian OptimisationBayesian OptimizationA Two-Stage Bayesian Optimisation for Automatic Tuning of an Unscented Kalman Filter for Vehicle Sideslip Angle Estimation
This paper presents a novel methodology to auto-tune an Unscented Kalman Filter (UKF). It involves using a Two-Stage Bayesian Optimisation (TSBO), based on a t-Student Process to optimise the process noise parameters of …
Bayesian OptimisationA penalisation method for batch multi-objective Bayesian optimisation with application in heat exchanger design
We present HIghly Parallelisable Pareto Optimisation (HIPPO) -- a batch acquisition function that enables multi-objective Bayesian optimisation methods to efficiently exploit parallel processing resources. Multi-Objectiv…
Bayesian OptimisationDiversityNeural Diffusion Processes
Neural network approaches for meta-learning distributions over functions have desirable properties such as increased flexibility and a reduced complexity of inference. Building on the successes of denoising diffusion mod…
Bayesian OptimisationDenoisingGaussian ProcessesMeta-LearningInformation-theoretic Inducing Point Placement for High-throughput Bayesian Optimisation
Sparse Gaussian Processes are a key component of high-throughput Bayesian optimisation (BO) loops -- an increasingly common setting where evaluation budgets are large and highly parallelised. By using representative subs…
Bayesian OptimisationGaussian ProcessesVocal Bursts Intensity PredictionSample-Efficient Optimisation with Probabilistic Transformer Surrogates
Faced with problems of increasing complexity, recent research in Bayesian Optimisation (BO) has focused on adapting deep probabilistic models as flexible alternatives to Gaussian Processes (GPs). In a similar vein, this …
Bayesian OptimisationGaussian ProcessesBayesian learning of effective chemical master equations in crowded intracellular conditions
Biochemical reactions inside living cells often occur in the presence of crowders -- molecules that do not participate in the reactions but influence the reaction rates through excluded volume effects. However the standa…
Bayesian OptimisationMono-surrogate vs Multi-surrogate in Multi-objective Bayesian Optimisation
Bayesian optimisation (BO) has been widely used to solve problems with expensive function evaluations. In multi-objective optimisation problems, BO aims to find a set of approximated Pareto optimal solutions. There are t…
Bayesian OptimisationR-MBO: A Multi-surrogate Approach for Preference Incorporation in Multi-objective Bayesian Optimisation
Many real-world multi-objective optimisation problems rely on computationally expensive function evaluations. Multi-objective Bayesian optimisation (BO) can be used to alleviate the computation time to find an approximat…
Bayesian Optimisation