Study improves Bayesian optimisation with ensemble transfer learning.
problem Improving sample efficiency in Bayesian optimisation of expensive functions.
method Empirical analysis of ensemble-based transfer learning methods and pipeline components.
result Two components (warm start initialisation and positive weight constraint) improve transfer learning Bayesian optimisation performance.
This paper explores optimising acquisition functions in Bayesian optimisation.
problem Optimising acquisition functions in Bayesian optimisation is challenging due to their non-convex nature.
method The authors derive compositional forms for acquisition functions and use them to recast maximisation as a compositional optimisation problem.
result The compositional approach to maximising acquisition functions shows empirical advantages across various tasks.
A new method learns priors for Bayesian optimisation to improve performance.
problem Bayesian optimisation tasks often assume strong similarity, which is violated in many cases.
method Replace strong similarity assumption with shape similarity, learn priors for hyperparameters.
result PLeBO and prior transfer find good inputs in fewer evaluations.
Wind farm layout optimisation tackles space constraints with Bayesian multi-objective approach.
problem Optimizing wind farm layout due to limited space and conflicting objectives.
method Set-based multi-objective Bayesian optimisation using Gaussian process.
result Demonstrates potential of set-based Bayesian multi-objective optimisation for wind farm layout.
Information-theoretic Bayesian optimisation techniques have demonstrated state-of-the-art performance in tackling important global optimisation problems. However, current information-theoretic approaches require many approximations in implementation, introduce often-prohibitive computational overhead and limit the choi…
Bayesian optimisation tackles expensive black-box functions with constraints.
problem Optimizing constrained black-box functions in machine learning and simulation.
method Proposes a new Knowledge Gradient acquisition function for constrained Bayesian optimisation.
result Demonstrates superior performance over four state-of-the-art constrained Bayesian optimisation algorithms.
Bayesian optimisation framework for graph functions.
problem Optimizing functions on graph structures efficiently.
method Learning suitable kernels and local modelling.
result Demonstrated effectiveness on synthetic and real-world graphs.
Bayesian optimisation algorithm for unknown search spaces with sub-linear regret.
problem Efficient optimisation of expensive black-box functions in unknown search spaces.
method Expands search space over iterations based on a hyperharmonic series, scales to high dimensions.
result Sub-linear regret growth for both algorithms.
Bayesian optimisation tackles high-dimensional categorical and mixed search spaces.
problem Bayesian optimisation on high-dimensional categorical and mixed search spaces is challenging.
method Combining local optimisation with a tailored kernel design.
result Empirically outperforms current baselines in performance and computational costs.
SOBER optimizes and quadrates efficiently in parallel for diverse tasks.
problem Scalability of batch Bayesian optimization and quadrature for expensive functions.
method Reformulates batch selection as a quadrature problem, balancing exploitation and exploration.
result SOBER outperforms 11 baselines on 12 tasks.
Unified Bayesian Optimisation for mixed variables improves performance.
problem Efficient optimisation of problems with both categorical and continuous variables.
method Derive value proposals from the Expected Improvement criterion to optimise both categorical and continuous variables under a single acquisition metric.
result Unified approach significantly outperforms existing methods across mixed-variable tasks.
Bayesian optimisation is improved by incorporating expert prior through space warping.
problem Cold start phase in expensive function optimisation.
method Prior distribution warps the search space around high probability regions of function optimum.
result Improves optimisation performance through acquisition agnostic approach.
Improved Bayesian optimisation method using randomised Gaussian process UCB.
problem Improving performance in Bayesian optimisation.
method Developed a modified Gaussian process upper confidence bound (GP-UCB) acquisition function.
result The method achieves better performance than GP-UCB in various problems.
New method optimises worst-case risk under model uncertainty.
problem Minimizing expected risk under posterior beliefs leads to sub-optimal decisions due to model uncertainty.
method Distributionally Robust Optimisation with Bayesian Ambiguity Sets (DRO-BAS)
result Improved out-of-sample robustness in the Newsvendor problem.
Bayesian optimisation has gained great popularity as a tool for optimising the parameters of machine learning algorithms and models. Somewhat ironically, setting up the hyper-parameters of Bayesian optimisation methods is notoriously hard. While reasonable practical solutions have been advanced, they can often fail to …
Optimizes data power control in cell-free networks for better spectral efficiency.
problem Maximizing overall spectral efficiency in cell-free networks with multi-objective optimisation.
method Applied scalable multi-objective Bayesian optimisation to solve convergence-time limitations.
result Improved radio resource management in cell-free networks.
Optimising black-box functions is important in many disciplines, such as tuning machine learning models, robotics, finance and mining exploration. Bayesian optimisation is a state-of-the-art technique for the global optimisation of black-box functions which are expensive to evaluate. At the core of this approach is a G…
Bayesian optimisation improves with fully-Bayesian treatment of hyperparameters.
problem Overconfident model predictions in BO due to ignoring hyperparameter uncertainty.
method Investigate FBBO using three approximate inference schemes compared to maximum likelihood approach.
result FBBO using EI with an ARD kernel leads to best performance in noise-free setting.
Improves Bayesian optimisation for engineering design problems with many variables.
problem Efficiently searching for global minima in high-dimensional design spaces.
method Integrates input and output data to identify a reduced latent subspace using probabilistic partial least squares.
result Significant improvements in convergence to the global minimum compared to existing methods.
New approach separates VAE and GP for better molecular optimisation.
problem Optimizing complex structured domains like molecular spaces using VAEs.
method Decouples VAE for structure generation and GP for predictive modelling, combining them with a Bayesian update rule.
result Improves identification of high-potential candidates in molecular optimisation.
BOE reformulates BO as a classifier for scalable batch optimisation.
problem Scalable batch optimisation of expensive functions.
method Reformulates BO as density-ratio estimation, removing need for explicit function prior.
result Theoretical guarantees and improved uncertainty estimates for batch optimisation.
New scalarizing functions improve multi-objective Bayesian optimisation.
problem Improving multi-objective Bayesian optimisation efficiency.
method Comparing two infill criteria based on hypervolume improvement.
result Effective scalarizing functions enhance hypervolume maximisation.
New method optimizes multiple points in Bayesian optimization efficiently.
problem Optimizing multiple points in expensive black-box functions.
method Reformulated BO as probability measure optimization, using convex gradient flows.
result Demonstrated effectiveness on various benchmarks compared to state-of-the-art methods.
New methods improve global optimisation for expensive functions using lookahead strategies.
problem Optimising expensive functions without gradient info in high dimensions.
method Nonmyopic acquisition strategies based on approximate dynamic programming.
result Nonmyopic methods outperform myopic approaches in various applications.
The notion of expense in Bayesian optimisation generally refers to the uniformly expensive cost of function evaluations over the whole search space. However, in some scenarios, the cost of evaluation for black-box objective functions is non-uniform since different inputs from search space may incur different costs for …
Bayesian optimisation framework for multi-objective decision-making from choice data.
problem Optimizing multi-objective functions via choice judgements.
method Gaussian process prior and novel likelihood model for choice data.
result Proposes a novel Bayesian framework for learning latent functions from choice data.
Efficient optimisation of black-box problems that comprise both continuous and categorical inputs is important, yet poses significant challenges. We propose a new approach, Continuous and Categorical Bayesian Optimisation (CoCaBO), which combines the strengths of multi-armed bandits and Bayesian optimisation to select …
Bayesian optimisation outperforms standard ML-II in small samples.
problem Standard ML-II fails in small-sample trials.
method Adopting fully Bayesian optimisation (FBO) as an alternative.
result FBO is more robust and practical than ML-II.
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 formulated as a functional optimisation problem. We develop a novel Bayesian optimisation framework for s…
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 has been applied successfully in many applications, modern optimisation tasks usher in new challeng…
GACBO optimizes unknown causal graphs with interventions.
problem Optimizing a target variable on an unknown causal graph with interventions.
method Graph Agnostic Causal Bayesian Optimisation (GACBO) seeks to balance exploitation and exploration of causal structures and functions.
result GACBO outperforms baselines in simulated and real-world applications.
Bayesian optimisation is a sample-efficient search methodology that holds great promise for accelerating drug and materials discovery programs. A frequently-overlooked modelling consideration in Bayesian optimisation strategies however, is the representation of heteroscedastic aleatoric uncertainty. In many practical a…
Bayesian optimisation (BO) is widely used to optimise stochastic black box functions. While most BO approaches focus on optimising conditional expectations, many applications require risk-averse strategies and alternative criteria accounting for the distribution tails need to be considered. In this paper, we propose ne…
Bayesian optimisation's mean function choice affects convergence speed.
problem The choice of mean function in Bayesian optimisation impacts convergence speed.
method Empirical investigation of 8 mean functions on 10 synthetic and 2 real-world problems.
result Using a constant mean function equal to the worst observed quality value promotes faster convergence.
We introduce a means of automating machine learning (ML) for big data tasks, by performing scalable stochastic Bayesian optimisation of ML algorithm parameters and hyper-parameters. More often than not, the critical tuning of ML algorithm parameters has relied on domain expertise from experts, along with laborious hand…
Investigates long-term performance of multi-fidelity Bayesian optimization.
problem Potential long-term under-performance of multi-fidelity Bayesian optimization.
method Simple benchmark study to investigate long-term performance.
result Under-performance of multi-fidelity Bayesian optimization in certain scenarios.
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 problem by developing an approach, Penalising Locally for Asynchronous Bayesian Optimisation on k…
New algorithm optimizes PAC-Bayes bound without surrogate loss.
problem Mismatch between optimisation objective and generalisation bound in stochastic neural networks.
method Proposes a novel training algorithm that optimizes the PAC-Bayesian bound directly.
result Empirical results show improved performance over existing PAC-Bayesian training methods.
BOP-Elites uses Bayesian Optimisation for QD search, improving efficiency and insight.
problem Finding diverse high-performing points from an objective function.
method Bayesian Optimisation and Gaussian Processes to model quality and diversity.
result Significantly more sample efficient and better at identifying niche solutions.
New method optimizes Gaussian process allocation for BO.
problem Existing methods for inducing point allocation in BO hinder performance.
method Proposes a new allocation strategy using quality-diversity decomposition.
result Demonstrates improved BO performance through local high-fidelity modeling.
Bayesian optimisation has been successfully applied to a variety of reinforcement learning problems. However, the traditional approach for learning optimal policies in simulators does not utilise the opportunity to improve learning by adjusting certain environment variables: state features that are unobservable and ran…
Bayesian optimization guided by experimenter intuition and beliefs.
problem Finding optimal functions with experimenter's beliefs incorporated.
method Sequential Subspace Search using Gaussian Process.
result Algorithm converges in sub-linear time with finite effective dimension.
This paper proposes using neural networks for Bayesian optimisation in machine learning.
problem Efficiently choosing the best model and its hyperparameters in machine learning applications.
method Uses neural networks to model distributions over functions, reformulating density-ratio estimation for approximate inference.
result Demonstrates the efficiency and tractability of using neural networks in Bayesian optimisation.
Bandit methods for black-box optimisation, such as Bayesian optimisation, are used in a variety of applications including hyper-parameter tuning and experiment design. Recently, \emph{multi-fidelity} methods have garnered considerable attention since function evaluations have become increasingly expensive in such appli…
MBORE optimizes multi-objective problems using density-ratio estimation.
problem Optimizing complex, multi-objective functions with expensive evaluations.
method Extends BORE to multi-objective Bayesian optimisation, using density-ratio estimation.
result MBORE outperforms BO on high-dimensional and real-world problems.
AEGiS optimizes expensive function evaluations asynchronously.
problem Optimizing expensive black-box functions efficiently.
method Asynchronous ε-Greedy Bayesian Optimisation combining greedy search and Thompson sampling.
result AEGiS outperforms existing asynchronous BO methods.
Bayesian surrogate models reduce uncertainty in high-dimensional design optimisation problems.
problem Uncertainty in high-dimensional inputs for complex computational models.
method Variational Bayesian inference for constructing statistical surrogates with Gaussian process priors and KL divergence for approximation.
result The RDVGP surrogate provides accurate and versatile approximations for robust structural optimisation.
Unified method for balancing simulation and data collection.
problem Finding optimal solutions with uncertain inputs.
method Bayesian Information Collection and Optimisation (BICO) procedure.
result Algorithm automatically determines optimal balance between optimisation and data collection.