Recent Advances in Bayesian Optimization
Bayesian optimization has emerged at the forefront of expensive black-box optimization due to its data efficiency. Recent years have witnessed a proliferation of studies on the development of new Bayesian optimization algorithms and their applications. Hence, this paper attempts to provide a comprehensive and updated survey of recent advances in Bayesian optimization and identify interesting open problems. We categorize the existing work on Bayesian optimization into nine main groups according to the motivations and focus of the proposed algorithms. For each category, we present the main advances with respect to the construction of surrogate models and adaptation of the acquisition functions. Finally, we discuss the open questions and suggest promising future research directions, in particular with regard to heterogeneity, privacy preservation, and fairness in distributed and federated optimization systems.
Code (0)
등록된 구현이 없습니다.
Tasks
Bayesian OptimizationFairnessSimilar Papers 제목 키워드 기반
Latency-Aware Neural Architecture Search with Multi-Objective Bayesian Optimization
When tuning the architecture and hyperparameters of large machine learning models for on-device deployment, it is desirable to understand the optimal trade-offs between on-device latency and model accuracy. In this work,…
Bayesian OptimizationNatural Language UnderstandingNeural Architecture SearchBayesian Coresets: Revisiting the Nonconvex Optimization Perspective
Bayesian coresets have emerged as a promising approach for implementing scalable Bayesian inference. The Bayesian coreset problem involves selecting a (weighted) subset of the data samples, such that the posterior infere…
Bayesian InferenceVBO-MI: A Fully Gradient-Based Bayesian Optimization Framework Using Variational Mutual Information Estimation
Many real-world tasks require optimizing expensive black-box functions accessible only through noisy evaluations, a setting commonly addressed with Bayesian optimization (BO). While Bayesian neural networks (BNNs) have r…
Gaussian ProcessesIncreasing the Scope as You Learn: Adaptive Bayesian Optimization in Nested Subspaces
Recent advances have extended the scope of Bayesian optimization (BO) to expensive-to-evaluate black-box functions with dozens of dimensions, aspiring to unlock impactful applications, for example, in the life sciences, …
Bayesian OptimizationNeural Architecture SearchBayesian Experimental Design of Magnetic Resonance Imaging Sequences
We show how improved sequences for magnetic resonance imaging can be found through automated optimization of Bayesian design scores. Combining recent advances in approximate Bayesian inference and natural image statistic…
Bayesian InferenceExperimental DesignVariational Inference