A new batch Bayesian optimization method for less expensive functions.
problem Traditional BO approaches are not suitable for less expensive functions due to high computational costs.
method Introduces a new exploration strategy using geometric distance and Sobol sequence to guide batch optimization.
result Demonstrates superior performance compared to existing methods when function evaluations are less expensive.
Bayesian method finds robust optima in expensive black-box functions.
problem Optimizing expensive black-box functions with sensitivity to inputs.
method Bayesian optimisation using Gaussian process prior and evolutionary algorithm for sampling and evaluation.
result Locating a region of design space with relatively insensitive performance to inputs.
Bayesian search optimizes exploration of feasible solutions under expensive constraints.
problem Identifying feasible solutions in computationally expensive constraint spaces.
method Bayesian models with an acquisition function for efficient exploration and exploitation.
result The proposed acquisition function improves the prediction of feasibility.
Optimizes expensive functions using adaptive RBF surrogate model.
problem Global optimization of expensive, possibly non-differentiable functions.
method Adaptive Radial Basis Function (RBF) surrogate model with uncertainty quantification.
result The proposed method identifies optimal points efficiently, especially for non-smooth surfaces.
A new Bayesian method optimizes time-dependent expensive functions with lookahead.
problem Maximizing a time-dependent, expensive oracle with limited evaluations.
method Recursive, two-step lookahead expected payoff (r2LEY) acquisition function.
result r2LEY outperforms myopic methods in synthetic and real-world datasets.
ε-shotgun optimizes expensive function evaluations in parallel.
problem Optimizing expensive black-box functions efficiently.
method ε-greedy batch Bayesian optimisation.
result ε-shotgun outperforms state-of-the-art batch methods.
Cost-aware multi-objective Bayesian optimization for non-uniformly expensive functions.
problem Non-uniform cost of function evaluations in Bayesian optimization.
method Introduces cost-aware constraints and a new acquisition function to optimize multi-objective functions with varying costs.
result Demonstrates improved optimization in hyperparameter tuning of neural networks and random forests.
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.
Novel CE-method variants reduce local minima convergence with fewer function evaluations.
problem Local minima and expensive function evaluations in optimization.
method Surrogate model-based CE-method variants to reduce local minima convergence.
result Surrogate model-based approach reduces local minima convergence using fewer function evaluations.
SVH-PSL uses Stein Variational Gradient Descent and Hypernetworks to improve Pareto set learning for expensive MOO.
problem Fragmented surrogate models and pseudo-local optima in expensive multi-objective optimization problems.
method SVH-PSL integrates Stein Variational Gradient Descent (SVGD) with Hypernetworks to address fragmentation and pseudo-local optima.
result SVH-PSL significantly improves the quality of the learned Pareto set, offering a promising solution for expensive MOO.
Enhanced MH algorithm reduces expensive function evaluations and improves sampling efficiency.
problem Computational expense of evaluating target distributions or likelihood functions, especially with big data.
method Accelerated MH algorithm using Bayesian optimization and Gaussian processes.
result Significant improvement in sampling efficiency and reduced function evaluations.
In many scientific and engineering applications, we are tasked with the maximisation of an expensive to evaluate black box function f. Traditional settings for this problem assume just the availability of this single function. However, in many cases, cheap approximations to f may be obtainable. For example, the exp…
PHS optimizes hyperparameters in parallel for expensive computations.
problem Optimizing hyperparameters in computationally expensive tasks.
method Bayesian optimization for parallel hyperparameter search.
result Efficient hyperparameter optimization on multiple instances.
Efficiently identifies key input variables for expensive functions using active learning.
problem Efficiently identify key input variables for expensive, black-box functions.
method Proposes novel active learning acquisition functions targeting derivative-based global sensitivity measures (DGSMs) under Gaussian process surrogate models.
result Active learning substantially enhances sample efficiency of DGSM estimation, especially with limited evaluation budgets.
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…
BDC uses Distance Correlation for efficient Bayesian optimization of expensive functions.
problem Efficiently optimizing expensive black-box functions with Bayesian methods.
method Integrates Bayesian optimization with Distance Correlation for automatic exploration and exploitation.
result BDC performs similarly to popular BO methods on benchmark tests and real terrain optimization.
BOKE optimizes expensive functions with reduced computational costs.
problem High computational cost of Gaussian process-based Bayesian optimization.
method Kernel regression and density-based exploration integrated into confidence bounds.
result BOKE achieves global convergence and superior computational efficiency.
POAP and pySOT improve surrogate optimization of expensive functions.
problem Optimizing expensive functions with concurrent evaluations.
method Event-driven asynchronous framework for optimization strategies.
result Asynchronous computation offers significant speed-up advantages.
USeMOC framework reduces expensive simulations for MO optimization with constraints.
problem Efficiently optimizing multi-objective problems with constraints using expensive function evaluations.
method USeMOC framework uses surrogate models to identify promising candidates and selects the best based on uncertainty.
result USeMOC achieves more than 90% reduction in function evaluations for circuit optimization.
Many objective Bayesian optimization tackles redundant objectives in expensive black-box functions.
problem Efficiently optimizing multiple expensive and noisy black-box functions with redundant objectives.
method Proposes a metric to identify redundant objectives and a Bayesian optimization algorithm to stop evaluating them.
result Reduces computational cost by stopping evaluation of redundant objectives, improving efficiency.
Gemini uses inexpensive measurements to correct biases in expensive property evaluations.
problem Accurate estimation of materials properties using expensive measurements is hindered in scientific discovery campaigns.
method Gemini is a data-driven model that corrects systematic biases between property evaluation methods using inexpensive measurements.
result Gemini reduces the number of expensive evaluations needed for Bayesian optimization in materials discovery.
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.
A new method for efficient optimization of expensive simulations on HPC.
problem Efficiently solving computationally expensive simulation-based optimization problems.
method Asynchronous parallel Bayesian optimization with budgeted computational resource.
result Improved efficiency and handling of constraints in optimization.
An adaptive dropout approach improves high-dimensional Bayesian optimization.
problem High-dimensional black-box optimization problems.
method Adaptive dropout of variables in the acquisition function.
result AdaDropout effectively tackles high-dimensional challenges and improves solution quality.
Bayesian SDOE method estimates QoIs from expensive black-box functions efficiently.
problem Estimating non-linear QoIs from expensive, unknown functions.
method Sequential design of experiments using Bayesian surrogate models and information gain.
result Method efficiently estimates QoIs with limited function evaluations.
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.
Active multi-source Bayesian quadrature improves efficiency in expensive function evaluations.
problem Efficiently solving integrals of expensive-to-evaluate functions using multiple related sources of information.
method Constructing cost-sensitive multi-source acquisition rates as an extension to vanilla Bayesian quadrature.
result Active multi-source Bayesian quadrature allocates budget more efficiently than vanilla Bayesian quadrature.
This paper develops a method to approximate the whole Pareto set for expensive multi-objective optimization.
problem Finding an approximate Pareto front with limited expensive evaluations.
method A novel learning-based method to approximate the whole Pareto set for multi-objective Bayesian optimization (MOBO).
result The method approximates the whole Pareto set, not just a finite set, for MOBO.
This work presents PESMOC, Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraints, an information-based strategy for the simultaneous optimization of multiple expensive-to-evaluate black-box functions under the presence of several constraints. PESMOC can hence be used to solve a wide range…
We study statistical calibration, i.e., adjusting features of a computational model that are not observable or controllable in its associated physical system. We focus on functional calibration, which arises in many manufacturing processes where the unobservable features, called calibration variables, are a function of…
Bayesian optimization tackles expensive cascade processes.
problem Optimizing multistage decision-making processes with expensive costs.
method Formulated as Bayesian optimization framework with two types of acquisition functions.
result Demonstrated effectiveness through numerical experiments and a solar cell simulator application.
Posterior sampling-based EI achieves sublinear regret bounds for expensive function optimization.
problem Theoretical analysis of expected improvement (EI) in Bayesian optimization.
method Randomized posterior sampling of EI.
result Achieves sublinear Bayesian cumulative regret bounds.
Framework optimizes expensive manufacturing processes efficiently.
problem Optimizing input parameters for advanced manufacturing methods.
method Bayesian optimization with tailored acquisition function and parallel acquisition.
result Framework efficiently finds optimal parameters with minimal process cost.
Efficiently optimizes expensive functions with multi-step lookahead using one-shot optimization.
problem Optimizing expensive functions with long-term impacts using myopic approaches.
method Formulated as nested optimization problems within a multi-step scenario tree, optimized in one-shot fashion.
result Multi-step expected improvement is computationally tractable and outperforms existing methods.
Optimizes expensive experiments by incorporating expert knowledge.
problem Expensive experiments require minimizing the number of trials.
method Bayesian optimization with posterior sampling of expert knowledge.
result Demonstrates significant efficiency gains in experiments and hyperparameter tuning.
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.
ColaBO accelerates optimization with user beliefs.
problem Optimizing expensive functions with limited prior knowledge.
method General Bayesian framework for incorporating user beliefs.
result Significant optimization acceleration with accurate prior information.
Improved MCMC sampling for expensive, irregular likelihoods.
problem Bayesian inference challenges with irregular, expensive likelihoods.
method Adapt subset samplers, introduce data-driven proxies, adaptive controller.
result Improved HINTS algorithm achieves best sampling error in fixed budget.
ECP optimizes expensive functions without knowing Lipschitz constant.
problem Optimizing expensive, non-convex functions with unknown Lipschitz constants.
method ECP minimizes evaluations by focusing on potentially optimal regions, eliminating Lipschitz constant estimation.
result Guaranteed no-regret performance and minimax-optimal regret bounds.
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.
MF BO combines MFO and BO to optimize expensive problems.
problem Expensive engineering design optimization problems.
method Gaussian process-based multi-fidelity surrogates and acquisition functions.
result Structured understanding of MF BO.
This paper analyzes local optimizers in Bayesian optimization for expensive functions.
problem Finding global optimizers in Bayesian optimization is challenging and time-consuming.
method The paper analyzes three acquisition functions (PI, EI, GP-UCB) and their local optimizers.
result Local optimizers can be used effectively in Bayesian optimization, reducing search time.
We propose a Bayesian optimization algorithm for objective functions that are sums or integrals of expensive-to-evaluate functions, allowing noisy evaluations. These objective functions arise in multi-task Bayesian optimization for tuning machine learning hyperparameters, optimization via simulation, and sequential des…
Study risk-minimizing insurance investments with taxes and expenses.
problem Determining optimal insurance investments in the presence of taxes and expenses.
method Introduced tax- and expense-modified risk-minimization, derived strategies, linked to decompositions, and established equivalence to artificial market approach.
result Equivalence to artificial market approach and consistency with classic risk-minimization.
GLIS uses IDW and RBF for efficient global optimization.
problem Expensive objective function in optimization problems.
method Combines inverse distance weighting and radial basis functions for acquisition function.
result Competitive and computationally lighter than Bayesian optimization.
In this paper, we focus on developing efficient sensitivity analysis methods for a computationally expensive objective function f(x) in the case that the minimization of it has just been performed. Here "computationally expensive" means that each of its evaluation takes significant amount of time, and therefore our m…
New method uses low-fidelity simulations to efficiently infer parameters of high-fidelity models.
problem Challenges in inferring parameters of computationally expensive high-fidelity models.
method Multifidelity simulation-based inference using transfer learning and adaptive selection of high-fidelity parameters.
result Significant reduction in the number of high-fidelity simulations required for inference.
We are focusing on bound constrained global optimization problems, whose objective functions are computationally expensive black-box functions and have multiple local minima. The recently popular Metric Stochastic Response Surface (MSRS) algorithm proposed by \cite{Regis2007SRBF} based on adaptive or sequential learnin…