Taking the Human Out of the Loop: A Review of Bayesian Optimization

2015/12/10 by Bobak Shahriari, Kevin Swersky, Ziyu Wang +2 · 6,017 citations
Computer Science · Decision Sciences · Mathematics · #Advanced Bandit Algorithms Research #Advanced Multi-Objective Optimization Algorithms #Artificial intelligence #Bayesian optimization #Bayesian probability #Computer science #Gaussian Processes and Bayesian Inference #Human-in-the-loop #Loop (graph theory) #Mathematics

paper · open access · doi:10.1109/jproc.2015.2494218

published in Proceedings of the IEEE 104(1), 148-175 (Institute of Electrical and Electronics Engineers)

openalex publication_date 2015/12/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

Big Data applications are typically associated with systems involving large numbers of users, massive complex software systems, and large-scale heterogeneous computing and storage architectures. The construction of such systems involves many distributed design choices. The end products (e.g., recommendation systems, medical analysis tools, real-time game engines, speech recognizers) thus involve many tunable configuration parameters. These parameters are often specified and hard-coded into the software by various developers or teams. If optimized jointly, these parameters can result in significant improvements. Bayesian optimization is a powerful tool for the joint optimization of design choices that is gaining great popularity in recent years. It promises greater automation so as to increase both product quality and human productivity. This review paper introduces Bayesian optimization, highlights some of its methodological aspects, and showcases a wide range of applications.

Citations

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• Simplifying Bayesian Optimization Via In-Context Direct Optimum Sampling
• Causal-PIK: Causality-based Physical Reasoning with a Physics-Informed Kernel
• BO-DBA: Query-Efficient Decision-Based Adversarial Attacks via Bayesian Optimization
• Tunneling Neural Perception and Logic Reasoning through Abductive Learning
• AutoSmart: An Efficient and Automatic Machine Learning framework for Temporal Relational Data
• Data-Driven Cellular Mobility Management via Bayesian Optimization and Reinforcement Learning
• Learning where to learn: Training data distribution optimization for scientific machine learning
• Improving LLM-based Global Optimization with Search Space Partitioning
• Geometric Graph Representations and Geometric Graph Convolutions for Deep Learning on Three-Dimensional (3D) Graphs
• A Derivative-Free Position Optimization Approach for Movable Antenna Multi-User Communication Systems
• Derivative free optimization via repeated classification
• OptiMindTune: A Multi-Agent Framework for Intelligent Hyperparameter Optimization
• Using Centroidal Voronoi Tessellations to Scale Up the Multi-dimensional Archive of Phenotypic Elites Algorithm
• Constrained Discrete Black-Box Optimization using Mixed-Integer Programming
• Hyperparameter Optimization via Interacting with Probabilistic Circuits
• Towards Autonomous Formulaic Alpha Discovery: An Evolutionary Computation Perspective
• High Dimensional Level Set Estimation with Bayesian Neural Network
• Harnessing Low-Fidelity Data to Accelerate Bayesian Optimization via Posterior Regularization
• Learning Flexible Forward Trajectories for Masked Molecular Diffusion
• Bayesian Optimization for Enhanced Language Models: Optimizing Acquisition Functions
• Sampling Humans for Optimizing Preferences in Coloring Artwork
• Bayesian Optimization with Approximate Set Kernels
• Online Preconditioning of Experimental Inkjet Hardware by Bayesian Optimization in Loop
• Bayesian Optimization for Dynamic Problems
• When to retrain a machine learning model
• Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian Processes
• Nonmyopic Gaussian Process Optimization with Macro-Actions
• Reasoning BO: Enhancing Bayesian Optimization with Long-Context Reasoning Power of LLMs
• Battery Storage Co-Optimization in Day-Ahead and Real-Time Markets with Bayesian Optimization
• Accelerating Bayesian Optimal Experimental Design via Local Radial Basis Functions: Application to Soft Material Characterization
• Cost-Efficient Online Hyperparameter Optimization
• Fast Information-theoretic Bayesian Optimisation
• Accelerating the Design of Multishell Upconverting Nanoparticles through Bayesian Optimization
• Optimizing Resource Allocation for QoS and Stability in Dynamic VLC-NOMA Networks via MARL
• LLM-guided DRL for Multi-tier LEO Satellite Networks with Hybrid FSO/RF Links
• Explaining Inference Queries with Bayesian Optimization
• Progressive Learning Algorithm for Efficient Person Re-Identification
• Bayesian Optimization of Pythia8 Tunes
• Black-box Adversarial Attacks with Bayesian Optimization
• Data-Driven Model Set Design for Model Averaged Particle Filter
• Agentic Bayesian Optimization through Surrogate-Augmented Autoresearch
• A Learning-Based Tune-Free Control Framework for Large Scale Autonomous Driving System Deployment
• Derivative-free optimization is competitive for aerodynamic design optimization in moderate dimensions
• DrMAD: Distilling Reverse-Mode Automatic Differentiation for Optimizing Hyperparameters of Deep Neural Networks
• 2022 Review of Data-Driven Plasma Science
• Technology readiness levels for machine learning systems
• A Very Brief and Critical Discussion on AutoML
• Model Inversion Networks for Model-Based Optimization
• Planar Robot Casting with Real2Sim2Real Self-Supervised Learning
• Combining Bayesian Inference and Reinforcement Learning for Agent Decision Making: A Review
• Lenient Regret and Good-Action Identification in Gaussian Process Bandits
• AutoOED: Automated Optimal Experiment Design Platform
• Generative Melody Composition with Human-in-the-Loop Bayesian Optimization
• Data-Centric Mixed-Variable Bayesian Optimization For Materials Design
• Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains
• Deep-ICE: the first globally optimal algorithm for minimizing 0-1 loss in two-layer ReLU and maxout networks
• Real-time Federated Evolutionary Neural Architecture Search
• BORE: Bayesian Optimization by Density-Ratio Estimation
• Sparse Spectrum Gaussian Process for Bayesian Optimization
• Learning Low-Dimensional Embeddings for Black-Box Optimization
• Hyperparameter Transfer Learning with Adaptive Complexity
• Wayfinder: Automated Operating System Specialization
• Adaptive Sampling for Automated Post-Disaster Rapid Damage Assessment via Level-Set Cost-Aware Bayesian Optimization
• Combinatorial 3D Shape Generation via Sequential Assembly
• ORTHOBO: Orthogonal Bayesian Hyperparameter Optimization
• PHS: A Toolbox for Parallel Hyperparameter Search
• A Batched Scalable Multi-Objective Bayesian Optimization Algorithm
• Graph Bayesian Optimization: Algorithms, Evaluations and Applications
• Machine learning enables roughness-driven inverse design of milling processes
• Learning Composable Energy Surrogates for PDE Order Reduction
• Surrogate Assisted Evolutionary Algorithm for Medium Scale Expensive Multi-Objective Optimisation Problems
• Stochastic Subspace Descent Accelerated via Bi-fidelity Line Search
• Generalised Bayes Updates with f-divergences through Probabilistic Classifiers
• A mobile robotic chemist
• Explanation format does not matter; but explanations do -- An Eggsbert study on explaining Bayesian Optimisation tasks
• Deciphering carnivoran competition for animal resources at the 1.46 Ma early Pleistocene site of Barranco León (Orce, Granada, Spain)
• Towards Automated Discovery: A Review of Generative Models, Multimodal Learning and Closed-Loop Workflows in Inverse Materials Design
• PINN - a Domain Decomposition Method for Bayesian Physics-Informed Neural Networks
• From Good to Great: Improving Memory Tiering Performance Through Parameter Tuning
• Mastering the game of Go without human knowledge
• BONSAI: Bayesian Optimization with Natural Simplicity and Interpretability
• Lookahead Acquisition Functions for Finite-Horizon Time-Dependent Bayesian Optimization and Application to Quantum Optimal Control
• SMOG: Scalable Meta-Learning for Multi-Objective Bayesian Optimization
• Exploiting Separability in Multi-Scale Grey-Box Bayesian Optimization
• Bayesian optimization with local search
• Knot Selection in Sparse Gaussian Processes
• Using models to improve optimizers for variational quantum algorithms
• Automating Nanoindentation: Optimizing Workflows for Precision and Accuracy
• Multifidelity Bayesian Optimization for Binomial Output
• Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization
• Transport Gaussian Processes for Regression
• Efficient assessment of process fidelity
• Bayesian Optimization for Iterative Learning
• Multi-Variable Batch Bayesian Optimization in Materials Research: Synthetic Data Analysis of Noise Sensitivity and Problem Landscape Effects
• Cellular Network Design for UAV Corridors via Data-driven High-dimensional Bayesian Optimization
• GRIMIP: A General Framework for Instance-Specific Configuration of MIP Solvers Using LLMs
• Preconditioning Natural and Second Order Gradient Descent in Quantum Optimization: A Performance Benchmark
• Identifying Untrustworthy Samples: Data Filtering for Open-domain Dialogues with Bayesian Optimization
• Detecting stable adsorbates of (1S)-camphor on Cu(111) with Bayesian optimization
• Robot Learning With Crash Constraints
• C-GLISp: Preference-Based Global Optimization under Unknown Constraints with Applications to Controller Calibration
• A fast and scalable framework for automated artifact recognition from EEG signals represented in scalp topographies of Independent Components
• A Similarity Measure of Gaussian Process Predictive Distributions
• Bayesian Optimization Assisted Meal Bolus Decision Based on Gaussian Processes Learning and Risk-Sensitive Control
• Robust Maximization of Non-Submodular Objectives
• Improving Bayesian Optimization for Portfolio Management with an Adaptive Scheduling
• Computational Workflows for Designing Input Devices
• Global optimization of atomic structures with gradient-enhanced Gaussian process regression
• BoGraph: Structured Bayesian Optimization From Logs for Expensive Systems with Many Parameters
• Towards Assessing the Impact of Bayesian Optimization's Own\n Hyperparameters
• Nudged elastic band calculations accelerated with Gaussian process regression
• Hyper-optimization with Gaussian Process and Differential Evolution Algorithm
• Bayesian optimization for the inverse scattering problem in quantum reaction dynamics
• Bayesian Learning-Based Adaptive Control for Safety Critical Systems
• Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search
• Data-Driven Permanent Magnet Temperature Estimation in Synchronous Motors With Supervised Machine Learning: A Benchmark
• Autonomous efficient experiment design for materials discovery with Bayesian model averaging
• Featuremetric benchmarking: Quantum computer benchmarks based on circuit features
• Saga: Capturing Multi-granularity Semantics from Massive Unlabelled IMU Data for User Perception
• An Adaptive Dropout Approach for High-Dimensional Bayesian Optimization
• ChartOptimiser: Task-driven Optimisation of Chart Designs
• Efficient Gradient-Enhanced Bayesian Optimizer with Comparisons to Conjugate-Gradient and Quasi-Newton Optimizers for Unconstrained Local Optimization
• Distilling and exploiting quantitative insights from Large Language Models for enhanced Bayesian optimization of chemical reactions
• Gradient-based Sample Selection for Faster Bayesian Optimization
• Optuna vs Code Llama: Are LLMs a New Paradigm for Hyperparameter Tuning?
• Multiobjective Optimization under Uncertainties using Conditional Pareto Fronts
• Rapid Bayesian optimisation for synthesis of short polymer fiber materials. [europepmc]
• Dissociating frontoparietal brain networks with neuroadaptive Bayesian optimization. [europepmc]
• Bayesian comparison of explicit and implicit causal inference strategies in multisensory heading perception. [europepmc]
• Ultranarrow-Band Wavelength-Selective Thermal Emission with Aperiodic Multilayered Metamaterials Designed by Bayesian Optimization. [europepmc]
• Analyzing Learned Molecular Representations for Property Prediction. [europepmc]
• Prediction of future gastric cancer risk using a machine learning algorithm and comprehensive medical check-up data: A case-control study. [europepmc]
• Toward clinical digital phenotyping: a timely opportunity to consider purpose, quality, and safety. [europepmc]
• Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges. [europepmc]
• Accurate Multiobjective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization. [europepmc]
• COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images. [europepmc]
• Data-Driven Strategies for Accelerated Materials Design. [europepmc]
• Practices and Applications of Convolutional Neural Network-Based Computer Vision Systems in Animal Farming: A Review. [europepmc]
• A data-driven metapopulation model for the Belgian COVID-19 epidemic: assessing the impact of lockdown and exit strategies. [europepmc]
• Machine learning applications in radiation oncology. [europepmc]
• Gaussian Process Regression for Materials and Molecules. [europepmc]
• Fast activation maximization for molecular sequence design. [europepmc]
• Closed-loop optimization of transcranial magnetic stimulation with electroencephalography feedback. [europepmc]
• Robotic search for optimal cell culture in regenerative medicine. [europepmc]
• A guided multiverse study of neuroimaging analyses. [europepmc]
• Autonomous Chemical Experiments: Challenges and Perspectives on Establishing a Self-Driving Lab. [europepmc]
• Maximizing mRNA vaccine production with Bayesian optimization. [europepmc]
• Technology readiness levels for machine learning systems. [europepmc]
• The Role of Machine Learning and Design of Experiments in the Advancement of Biomaterial and Tissue Engineering Research. [europepmc]
• A Brief Introduction to Chemical Reaction Optimization. [europepmc]
• Data-Driven Methods for Accelerating Polymer Design. [europepmc]

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