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About 10,000 papers for “Bayesian optimization”

Bayesian Optimization

Peng Liu · Apress eBooks · 2023

This book covers the implementation of popular Bayesian optimization techniques using Python, with practical examples to illustrate various concepts.

Cited by 16Open access

Bayesian Optimization

Julien Bect, Emmanuel Vázquez · HAL (Le Centre pour la Communication Scientifique Directe) · 2017

Cited by 0

Bayesian Optimization

Ivo Couckuy, Sebastian Rojas Gonzalez, Juergen Branke et al. · 2023

Cited by 9Open access

Bayesian optimization.

Kevin P. Greenman (17703253), Ava P. Amini (15853140), Kevin K. Yang (4528963) · Figshare · 2025

<p>(A-C) Bayesian optimization in protein sequence-function prediction. % of top-100 scores in training set found for the CNN ensemble, CNN evidential, and GP methods evaluated on the AAV/Random (A), Meltome/Random (B), and GB1/Random (C) splits. The “greedy” strategy acquired sequences with the best predicted property values. The “UCB” and “TS” strategies acquired sequences based on the upper confidence bound (UCB) and Thompson sampling (TS) approaches, respectively. The “random” strategy acquired sequences with all unseen points having equal probabilities. See the Uncertainty Methods section for an explanation of why GP experiments for the AAV landscape were not feasible. Note that in several plots, including the Gaussian process plots for Meltome and GB1 and the evidential plot for Meltome, the “greedy” strategy performance is nearly identical to and is covered up by the “UCB” strategy.</p>

Cited by 0Open access

Bayesian Optimization

Roman Garnett · Cambridge University Press eBooks · 2023

Bayesian optimization is a methodology for optimizing expensive objective functions that has proven success in the sciences, engineering, and beyond. This timely text provides a self-contained and comprehensive introduction to the subject, starting from scratch and carefully developing all the key ideas along the way. This bottom-up approach illuminates unifying themes in the design of Bayesian optimization algorithms and builds a solid theoretical foundation for approaching novel situations. The core of the book is divided into three main parts, covering theoretical and practical aspects of Gaussian process modeling, the Bayesian approach to sequential decision making, and the realization and computation of practical and effective optimization policies. Following this foundational material, the book provides an overview of theoretical convergence results, a survey of notable extensions, a comprehensive history of Bayesian optimization, and an extensive annotated bibliography of applications.

Cited by 522

Bayesian optimization

Peter Kostovčík · Digital Repository (National Repository of Grey Literature) · 2017

Optimization is an important part of mathematics and is mostly used for practical applications. For specific types of objective functions, a lot of different methods exist. A method to use when the objective is unknown and/or expensive can be difficult to determine. One of the answers is bayesian optimization, which instead of direct optimization creates a probabilistic model and uses it to constructs easily optimizable auxiliary function. It is an iterative method that uses information from previous iterations to find new point in which the objective is evaluated and tries to find the optimum within a fewer iterations. This thesis introduces bayesian optimization, suma- rizes its different approaches in lower and higher dimensions and shows when to use it suitably. An important part of the thesis is my own optimization algorithm which is applied to different practical problems - e.g. parameter optimization in machine learning algorithm. 1

Cited by 0

Bayesian Optimization

Juergen Branke, Sebastian Rojas Gonzalez, Ivo Couckuyt · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2024

Cited by 13

Bayesian Optimization

Juergen Branke, Sebastian Rojas Gonzalez, Ivo Couckuyt · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2025

Cited by 0Open access

Bayesian Optimization Overview

Peng Liu · Apress eBooks · 2023

As the name suggests, Bayesian optimization is an area that studies optimization problems using the Bayesian approach. Optimization aims at locating the optimal objective value (i.e., a global maximum or minimum) of all possible values or the corresponding location of the optimum in the environment (the search domain). The search process starts at a specific initial location and follows a particular policy to iteratively guide the following sampling locations, collect new observations, and refresh the guiding policy.

Cited by 3

Bayesian optimization process.

Myunghee Kim (695702), Ye Ding (40594), Philippe Malcolm (281731) et al. · Figshare · 2017

<p>After collecting two data points, the posterior distribution was calculated (a, top) and expected improvement (EI) was estimated (a, bottom). By maximizing EI, the new candidate point was chosen and the cost was obtained. A new posterior is then computed (b) and the process was repeated until the termination criterion met (c). Above, we used hypothetical data and reduced the exploration points from three to two for a simple explanation.</p>

Cited by 0Open access

Bayesian Optimization-GRP

Tengyu Zhang · Zenodo (CERN European Organization for Nuclear Research) · 2026

Cited by 0Open access

Bayesian robust optimization

Le, Hoai Phuong · Warwick Research Archive Portal (University of Warwick) · 2025

When optimizing complex systems, the implemented solution can often not be guaranteed to exactly follow the design specification, or there is uncertainty about the environment in which the solution operates. In such cases, one is often looking for solutions that are not merely good but also robust. Robustness in this context means some degree of insensitivity to small disturbances of the environment or the design variables. In this thesis, we are optimizing the expected performance of a solution given disturbances of the input and noise on the output. Bayesian optimization is used throughout the thesis. Based on the well-known Knowledge Gradient acquisition function, we first develop a novel acquisition function for searching robust solutions of black-box objectives. We also study a naive method for finding robust solutions by approximating the robustness measure directly. Furthermore, we consider reliability, which is often critical in engineering. We next examine how these two methods work in the case of uncertain input when the input may be uncontrollable and unobservable. We propose some improvements to the latter method and compare with the former one under uncertain inputs. We then extend our novel method to multiobjective optimization of two independent objectives and apply it to a real-world problem. In addition, we derive the analytical formulas for mean and variance of the robustness measure and consider both when optimizing their linear combination. Finally, we focus on studying one important stage of Bayesian optimization: optimizing the acquisition function. We examine the landscapes and the difficulty of optimizing two popular acquisition functions: Expected Improvement and Knowledge Gradient, and propose a transfer learning method with the aim of improving the quality of optimization.

Cited by 0

Ensemble Bayesian Optimization

Zi Wang, Clement Gehring, Pushmeet Kohli et al. · 2017

Bayesian Optimization (BO) has been shown to be a very effective paradigm for tackling hard black-box and non-convex optimization problems encountered in Machine Learning. Despite these successes, the computational complexity of the underlying function approximation has restricted the use of BO to problems that can be handled with less than a few thousand function evaluations. Harder problems like those involving functions operating in very high dimensional spaces may require hundreds of thousands or millions of evaluations or more and become computationally intractable to handle using standard Bayesian Optimization methods. In this paper, we propose Ensemble Bayesian Optimization (EBO) to overcome this problem. Unlike conventional BO methods that operate on a single posterior GP model, EBO works with an ensemble of posterior GP models. Further, we represent each GP model using tile coding random features and an additive function structure. Our approach generates speedups by parallelizing the time consuming hyper-parameter posterior inference and functional evaluations on hundreds of cores and aggregating the models in every iteration of BO. Our extensive experimental evaluation shows that EBO can speed up the posterior inference between 2-3 orders of magnitude (400 times in one experiment) compared to the state-of-the-art by putting data into Mondrian bins without sacrificing the sample quality. We demonstrate the ability of EBO to handle sample-intensive hard optimization problems by applying it to a rover navigation problem with tens of thousands of observations.

Cited by 0

Bayesian optimization example.

Logan L. Grado (6058535), Matthew D. Johnson (450496), Theoden I. Netoff (3153273) · Figshare · 2018

<p>Three iterations of Bayesian optimization minimizing a 1D function. The figure shows a Gaussian process (GP) approximation (solid black line and blue shaded region) of the underlying objective function (dotted black line). The figure also shows the acquisition function (green). The acquisition function (GP-LCB) is the difference of the mean and variance of the GP (multiplied by a constant), which Bayesian optimization minimizes to determine where to sample next.</p>

Cited by 0Open access