Papers Bayesian Optimization
“Bayesian Optimization” 태그가 달린 논문 1,900편 · 필터 해제
Vanilla Bayesian Optimization Performs Great in High Dimensions
High-dimensional problems have long been considered the Achilles' heel of Bayesian optimization algorithms. Spurred by the curse of dimensionality, a large collection of algorithms aim to make it more performant in this …
Bayesian OptimizationEnhancing Gaussian Process Surrogates for Optimization and Posterior Approximation via Random Exploration
This paper proposes novel noise-free Bayesian optimization strategies that rely on a random exploration step to enhance the accuracy of Gaussian process surrogate models. The new algorithms retain the ease of implementat…
Bayesian InferenceBayesian OptimizationBayesian optimization as a flexible and efficient design framework for sustainable process systems
Bayesian optimization (BO) is a powerful technology for optimizing noisy expensive-to-evaluate black-box functions, with a broad range of real-world applications in science, engineering, economics, manufacturing, and bey…
Bayesian OptimizationBayesian Optimization through Gaussian Cox Process Models for Spatio-temporal Data
Bayesian optimization (BO) has established itself as a leading strategy for efficiently optimizing expensive-to-evaluate functions. Existing BO methods mostly rely on Gaussian process (GP) surrogate models and are not ap…
Bayesian OptimizationExplainable Bayesian Optimization
Manual parameter tuning of cyber-physical systems is a common practice, but it is labor-intensive. Bayesian Optimization (BO) offers an automated alternative, yet its black-box nature reduces trust and limits human-BO co…
Bayesian OptimizationHyperparameter OptimizationMultiobjective OptimizationRun-to-Run Control With Bayesian Optimization for Soft Landing of Short-Stroke Reluctance Actuators
There is great interest in minimizing the impact forces of reluctance actuators during commutations, in order to reduce contact bouncing, acoustic noise and mechanical wear. In this regard, a run-to-run control algorithm…
Bayesian OptimizationDimensionality ReductionIntegrating Human Expertise in Continuous Spaces: A Novel Interactive Bayesian Optimization Framework with Preference Expected Improvement
Interactive Machine Learning (IML) seeks to integrate human expertise into machine learning processes. However, most existing algorithms cannot be applied to Realworld Scenarios because their state spaces and/or action s…
Bayesian OptimizationQuantum Architecture Search with Unsupervised Representation Learning
Unsupervised representation learning presents new opportunities for advancing Quantum Architecture Search (QAS) on Noisy Intermediate-Scale Quantum (NISQ) devices. QAS is designed to optimize quantum circuits for Variati…
Bayesian OptimizationNeural Architecture SearchRepresentation LearningLearning-assisted Stochastic Capacity Expansion Planning: A Bayesian Optimization Approach
Solving large-scale capacity expansion problems (CEPs) is central to cost-effective decarbonization of regional-scale energy systems. To ensure the intended outcomes of CEPs, modeling uncertainty due to weather-dependent…
Bayesian OptimizationStochastic OptimizationTime SeriesSimulation Based Bayesian Optimization
Bayesian Optimization (BO) is a powerful method for optimizing black-box functions by combining prior knowledge with ongoing function evaluations. BO constructs a probabilistic surrogate model of the objective function g…
Bayesian OptimizationCombinatorial OptimizationGaussian ProcessesA Unified Gaussian Process for Branching and Nested Hyperparameter Optimization
Choosing appropriate hyperparameters plays a crucial role in the success of neural networks as hyper-parameters directly control the behavior and performance of the training algorithms. To obtain efficient tuning, Bayesi…
Bayesian OptimizationHyperparameter OptimizationAutomatic dimensionality reduction of Twin-in-the-Loop Observers
State-of-the-art vehicle dynamics estimation techniques usually share one common drawback: each variable to estimate is computed with an independent, simplified filtering module. These modules run in parallel and need to…
Bayesian OptimizationDimensionality ReductionDeep learning enhanced mixed integer optimization: Learning to reduce model dimensionality
This work introduces a framework to address the computational complexity inherent in Mixed-Integer Programming (MIP) models by harnessing the potential of deep learning. By employing deep learning, we construct problem-s…
Bayesian OptimizationDeep LearningMulti-Label ClassificationSchedulingKernelized Normalizing Constant Estimation: Bridging Bayesian Quadrature and Bayesian Optimization
In this paper, we study the problem of estimating the normalizing constant $\int e^{-\lambda f(x)}dx$ through queries to the black-box function $f$, where $f$ belongs to a reproducing kernel Hilbert space (RKHS), and $\l…
Bayesian OptimizationClosed-loop Identification of a MSW Grate Incinerator using Bayesian Optimization for Selecting Model Inputs and Structure
The creation of low-order dynamic models for complex industrial systems is complicated by disturbances and limited sensor accuracy. This work presents a system identification procedure that uses machine learning methods …
Bayesian OptimizationFLUEOptimized Ensemble Model Towards Secured Industrial IoT Devices
The continued growth in the deployment of Internet-of-Things (IoT) devices has been fueled by the increased connectivity demand, particularly in industrial environments. However, this has led to an increase in the number…
Bayesian OptimizationEnsemble LearningmodelA Multi-objective Newton Optimization Algorithm for Hyper-Parameter Search
This study proposes a Newton based multiple objective optimization algorithm for hyperparameter search. The first order differential (gradient) is calculated using finite difference method and a gradient matrix with vect…
Bayesian Optimizationobject-detectionObject DetectionSeqNAS: Neural Architecture Search for Event Sequence Classification
Neural Architecture Search (NAS) methods are widely used in various industries to obtain high quality taskspecific solutions with minimal human intervention. Event Sequences find widespread use in various industrial appl…
Bayesian OptimizationClassificationFault DiagnosisFraud Detection+3Improving sample efficiency of high dimensional Bayesian optimization with MCMC
Sequential optimization methods are often confronted with the curse of dimensionality in high-dimensional spaces. Current approaches under the Gaussian process framework are still burdened by the computational complexity…
Bayesian OptimizationThompson SamplingAn Invariant Information Geometric Method for High-Dimensional Online Optimization
Sample efficiency is crucial in optimization, particularly in black-box scenarios characterized by expensive evaluations and zeroth-order feedback. When computing resources are plentiful, Bayesian optimization is often f…
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