Papers Bayesian Optimization
“Bayesian Optimization” 태그가 달린 논문 1,900편 · 필터 해제
Geometric Parameter Estimations of Perovskite Solar Cells Based on Optical Simulations
This paper presents a non-invasive approach to estimate the layer thicknesses of perovskite solar cells. The thicknesses are predicted by a convolutional neural network that leverages the external quantum efficiency of a…
Bayesian OptimizationHyperArm Bandit Optimization: A Novel approach to Hyperparameter Optimization and an Analysis of Bandit Algorithms in Stochastic and Adversarial Settings
This paper explores the application of bandit algorithms in both stochastic and adversarial settings, with a focus on theoretical analysis and practical applications. The study begins by introducing bandit problems, dist…
Bayesian OptimizationComputational EfficiencyEfficient ExplorationHyperparameter OptimizationAn Analysis of Safety Guarantees in Multi-Task Bayesian Optimization
This paper addresses the integration of additional information sources into a Bayesian optimization framework while ensuring that safety constraints are satisfied. The interdependencies between these information sources …
Bayesian OptimizationLarge Scale Multi-Task Bayesian Optimization with Large Language Models
In multi-task Bayesian optimization, the goal is to leverage experience from optimizing existing tasks to improve the efficiency of optimizing new ones. While approaches using multi-task Gaussian processes or deep kernel…
Bayesian OptimizationGaussian ProcessesPerformance-driven Constrained Optimal Auto-Tuner for MPC
A key challenge in tuning Model Predictive Control (MPC) cost function parameters is to ensure that the system performance stays consistently above a certain threshold. To address this challenge, we propose a novel metho…
Autonomous RacingBayesian OptimizationModel Predictive ControlFully Unsupervised Annotation of C. Elegans
In this work we present a novel approach for unsupervised multi-graph matching, which applies to problems for which a Gaussian distribution of keypoint features can be assumed. We leverage cycle consistency as loss for s…
Bayesian OptimizationGraph MatchingSelf-Supervised LearningBayesian Optimization for Robust Identification of Ornstein-Uhlenbeck Model
This paper deals with the identification of the stochastic Ornstein-Uhlenbeck (OU) process error model, which is characterized by an inverse time constant, and the unknown variances of the process and observation noises.…
Bayesian Optimizationglobal-optimizationSample-aware Adaptive Structured Pruning for Large Language Models
Large language models (LLMs) have achieved outstanding performance in natural language processing, but enormous model sizes and high computational costs limit their practical deployment. Structured pruning can effectivel…
Bayesian OptimizationClustering-based Meta Bayesian Optimization with Theoretical Guarantee
Bayesian Optimization (BO) is a well-established method for addressing black-box optimization problems. In many real-world scenarios, optimization often involves multiple functions, emphasizing the importance of leveragi…
Bayesian OptimizationClusteringHyperparameter OptimizationMeta-LearningULTHO: Ultra-Lightweight yet Efficient Hyperparameter Optimization in Deep Reinforcement Learning
Hyperparameter optimization (HPO) is a billion-dollar problem in machine learning, which significantly impacts the training efficiency and model performance. However, achieving efficient and robust HPO in deep reinforcem…
Bayesian OptimizationDeep Reinforcement LearningHyperparameter OptimizationReinforcement Learning (RL)BARK: A Fully Bayesian Tree Kernel for Black-box Optimization
We perform Bayesian optimization using a Gaussian process perspective on Bayesian Additive Regression Trees (BART). Our BART Kernel (BARK) uses tree agreement to define a posterior over piecewise-constant functions, and …
Bayesian OptimizationGaussian Processesglobal-optimizationUncertainty QuantificationMOHPER: Multi-objective Hyperparameter Optimization Framework for E-commerce Retrieval System
E-commerce search optimization has evolved to include a wider range of metrics that reflect user engagement and business objectives. Modern search frameworks now incorporate advanced quality features, such as sales count…
Bayesian OptimizationHyperparameter OptimizationRetrievalOn the Data-Driven Modeling of Price-Responsive Flexible Loads: Formulation and Algorithm
The flexible loads in power systems, such as interruptible and transferable loads, are critical flexibility resources for mitigating power imbalances. Despite their potential, accurate modeling of these loads is a challe…
Bayesian Optimizationparameter estimationDeterministic Global Optimization of the Acquisition Function in Bayesian Optimization: To Do or Not To Do?
Bayesian Optimization (BO) with Gaussian Processes relies on optimizing an acquisition function to determine sampling. We investigate the advantages and disadvantages of using a deterministic global solver (MAiNGO) compa…
Bayesian OptimizationCPUGaussian Processesglobal-optimizationImproving Oil Slick Trajectory Simulations with Bayesian Optimization
Accurate simulations of oil spill trajectories are essential for supporting practitioners' response and mitigating environmental and socioeconomic impacts. Numerical models, such as MEDSLIK-II, simulate advection, disper…
Bayesian OptimizationWeighted Euclidean Distance Matrices over Mixed Continuous and Categorical Inputs for Gaussian Process Models
Gaussian Process (GP) models are widely utilized as surrogate models in scientific and engineering fields. However, standard GP models are limited to continuous variables due to the difficulties in establishing correlati…
Bayesian OptimizationConstrained multi-fidelity Bayesian optimization with automatic stop condition
Bayesian optimization (BO) is increasingly employed in critical applications to find the optimal design with minimal cost. While BO is known for its sample efficiency, relying solely on costly high-fidelity data can stil…
Bayesian OptimizationPABBO: Preferential Amortized Black-Box Optimization
Preferential Bayesian Optimization (PBO) is a sample-efficient method to learn latent user utilities from preferential feedback over a pair of designs. It relies on a statistical surrogate model for the latent function, …
Bayesian OptimizationMeta-LearningA physics-informed Bayesian optimization method for rapid development of electrical machines
Advanced slot and winding designs are imperative to create future high performance electrical machines (EM). As a result, the development of methods to design and improve slot filling factor (SFF) has attracted considera…
Bayesian OptimizationGaussian ProcessesPhysics-informed machine learningslot-filling+1Few-shot crack image classification using clip based on bayesian optimization
This study proposes a novel few-shot crack image classification model based on CLIP and Bayesian optimization. By combining multimodal information and Bayesian approach, the model achieves efficient classification of cra…
Bayesian OptimisationBayesian OptimizationClassificationimage-classification+1