Papers Bayesian Optimization
“Bayesian Optimization” 태그가 달린 논문 1,900편 · 필터 해제
Regional Expected Improvement for Efficient Trust Region Selection in High-Dimensional Bayesian Optimization
Real-world optimization problems often involve complex objective functions with costly evaluations. While Bayesian optimization (BO) with Gaussian processes is effective for these challenges, it suffers in high-dimension…
Bayesian OptimizationDimensionality ReductionGaussian ProcessesUsing machine learning to inform harvest control rule design in complex fishery settings
In fishery science, harvest management of size-structured stochastic populations is a long-standing and difficult problem. Rectilinear precautionary policies based on biomass and harvesting reference points have now beco…
Bayesian OptimizationManagementReinforcement Learning (RL)Ba-ZebraConf: A Three-Dimension Bayesian Framework for Efficient System Troubleshooting
The proliferation of heterogeneous configurations in distributed systems presents significant challenges in ensuring stability and efficiency. Misconfigurations, driven by complex parameter interdependencies, can lead to…
Bayesian OptimizationExplainable Fuzzy Neural Network with Multi-Fidelity Reinforcement Learning for Micro-Architecture Design Space Exploration
With the continuous advancement of processors, modern micro-architecture designs have become increasingly complex. The vast design space presents significant challenges for human designers, making design space exploratio…
Bayesian OptimizationDecision MakingEnsemble LearningBayesian Optimization via Continual Variational Last Layer Training
Gaussian Processes (GPs) are widely seen as the state-of-the-art surrogate models for Bayesian optimization (BO) due to their ability to model uncertainty and their performance on tasks where correlations are easily capt…
Bayesian OptimizationGaussian ProcessesNon-Myopic Multi-Objective Bayesian Optimization
We consider the problem of finite-horizon sequential experimental design to solve multi-objective optimization (MOO) of expensive black-box objective functions. This problem arises in many real-world applications, includ…
Bayesian OptimizationExperimental DesignNoise-Aware Bayesian Optimization Approach for Capacity Planning of the Distributed Energy Resources in an Active Distribution Network
The growing penetration of renewable energy sources (RESs) in active distribution networks (ADNs) leads to complex and uncertain operation scenarios, resulting in significant deviations and risks for the ADN operation. I…
Bayesian OptimizationBayesian optimized deep ensemble for uncertainty quantification of deep neural networks: a system safety case study on sodium fast reactor thermal stratification modeling
Accurate predictions and uncertainty quantification (UQ) are essential for decision-making in risk-sensitive fields such as system safety modeling. Deep ensembles (DEs) are efficient and scalable methods for UQ in Deep N…
Bayesian OptimizationUncertainty QuantificationMonte Carlo Tree Search based Space Transfer for Black-box Optimization
Bayesian optimization (BO) is a popular method for computationally expensive black-box optimization. However, traditional BO methods need to solve new problems from scratch, leading to slow convergence. Recent studies tr…
Bayesian OptimizationTransfer LearningBayesian Data Augmentation and Training for Perception DNN in Autonomous Aerial Vehicles
Learning-based solutions have enabled incredible capabilities for autonomous systems. Autonomous vehicles, both aerial and ground, rely on DNN for various integral tasks, including perception. The efficacy of supervised …
Autonomous VehiclesBayesian OptimizationData AugmentationSynthetic Data GenerationStealthy and Robust Backdoor Attack against 3D Point Clouds through Additional Point Features
Recently, 3D backdoor attacks have posed a substantial threat to 3D Deep Neural Networks (3D DNNs) designed for 3D point clouds, which are extensively deployed in various security-critical applications. Although the exis…
Backdoor AttackBayesian OptimizationBayesian Optimization of Antibodies Informed by a Generative Model of Evolving Sequences
To build effective therapeutics, biologists iteratively mutate antibody sequences to improve binding and stability. Proposed mutations can be informed by previous measurements or by learning from large antibody databases…
Bayesian OptimizationLanguage ModelingLanguage ModellingLarge Language ModelEpidemiological Model Calibration via Graybox Bayesian Optimization
In this study, we focus on developing efficient calibration methods via Bayesian decision-making for the family of compartmental epidemiological models. The existing calibration methods usually assume that the compartmen…
Bayesian OptimizationDecision MakingGaussian ProcessesmodelHyperband-based Bayesian Optimization for Black-box Prompt Selection
Optimal prompt selection is crucial for maximizing large language model (LLM) performance on downstream tasks. As the most powerful models are proprietary and can only be invoked via an API, users often manually refine p…
Bayesian OptimizationLarge Language ModelMoSH: Modeling Multi-Objective Tradeoffs with Soft and Hard Bounds
Countless science and engineering applications in multi-objective optimization (MOO) necessitate that decision-makers (DMs) select a Pareto-optimal solution which aligns with their preferences. Evaluating individual solu…
Bayesian OptimizationLarge Language ModelElucidating microstructural influences on fatigue behavior for additively manufactured Hastelloy X using Bayesian-calibrated crystal plasticity model
Crystal plasticity (CP) modeling is a vital tool for predicting the mechanical behavior of materials, but its calibration involves numerous (>8) constitutive parameters, often requiring time-consuming trial-and-error met…
Bayesian OptimizationSemi-automated transmission control for motorcycle gearshift: design, data-driven tuning and experimental validation
This brief addresses the gearshifting problem for Semi-Automated Manual Transmissions (S-AMT) in powered two-wheelers, a powertrain setup that allows fast and smooth gear shifts with minimal modifications to the traditio…
Bayesian OptimizationUn-evaluated Solutions May Be Valuable in Expensive Optimization
Expensive optimization problems (EOPs) are prevalent in real-world applications, where the evaluation of a single solution requires a significant amount of resources. In our study of surrogate-assisted evolutionary algor…
Bayesian OptimizationEvolutionary AlgorithmsAsynchronous Batch Bayesian Optimization with Pipelining Evaluations for Experimental Resource$\unicode{x2013}$constrained Conditions
Bayesian optimization is efficient even with a small amount of data and is used in engineering and in science, including biology and chemistry. In Bayesian optimization, a parameterized model with an uncertainty is fitte…
Bayesian OptimizationBenchmarkingParetoFlow: Guided Flows in Multi-Objective Optimization
In offline multi-objective optimization (MOO), we leverage an offline dataset of designs and their associated labels to simultaneously minimize multiple objectives. This setting more closely mirrors complex real-world pr…
Bayesian OptimizationEvolutionary Algorithms