Papers Bayesian Optimization
“Bayesian Optimization” 태그가 달린 논문 1,900편 · 필터 해제
Beyond Regrets: Geometric Metrics for Bayesian Optimization
Bayesian optimization is a principled optimization strategy for a black-box objective function. It shows its effectiveness in a wide variety of real-world applications such as scientific discovery and experimental design…
Bayesian OptimizationExperimental Designscientific discoveryAccelerating Black-Box Molecular Property Optimization by Adaptively Learning Sparse Subspaces
Molecular property optimization (MPO) problems are inherently challenging since they are formulated over discrete, unstructured spaces and the labeling process involves expensive simulations or experiments, which fundame…
Bayesian Optimizationmolecular representationEfficient Hyperparameter Optimization with Adaptive Fidelity Identification
Hyperparameter Optimization and Neural Architecture Search are powerful in attaining state-of-the-art machine learning models with Bayesian Optimization (BO) standing out as a mainstream method. Extending BO into the…
Bayesian OptimizationHyperparameter OptimizationNeural Architecture SearchPG-LBO: Enhancing High-Dimensional Bayesian Optimization with Pseudo-Label and Gaussian Process Guidance
Variational Autoencoder based Bayesian Optimization (VAE-BO) has demonstrated its excellent performance in addressing high-dimensional structured optimization problems. However, current mainstream methods overlook the po…
Bayesian OptimizationPseudo LabelBayesian Inverse Transfer in Evolutionary Multiobjective Optimization
Transfer optimization enables data-efficient optimization of a target task by leveraging experiential priors from related source tasks. This is especially useful in multiobjective optimization settings where a set of tra…
Bayesian OptimizationMultiobjective OptimizationTransfer LearningCross-Layer Optimization for Fault-Tolerant Deep Learning
Fault-tolerant deep learning accelerator is the basis for highly reliable deep learning processing and critical to deploy deep learning in safety-critical applications such as avionics and robotics. Since deep learning i…
Bayesian OptimizationDeep LearningQuantizationBayesian optimized physics-informed neural network for estimating wave propagation velocities
In this paper, we propose a novel inverse parameter estimation approach called Bayesian optimized physics-informed neural network (BOPINN). In this study, a PINN solves the partial differential equation (PDE), whereas Ba…
Bayesian Optimizationparameter estimationDomain Invariant Learning for Gaussian Processes and Bayesian Exploration
Out-of-distribution (OOD) generalization has long been a challenging problem that remains largely unsolved. Gaussian processes (GP), as popular probabilistic model classes, especially in the small data regime, presume st…
Bayesian OptimizationGaussian ProcessesStochastic Bayesian Optimization with Unknown Continuous Context Distribution via Kernel Density Estimation
Bayesian optimization (BO) is a sample-efficient method and has been widely used for optimizing expensive black-box functions. Recently, there has been a considerable interest in BO literature in optimizing functions tha…
Bayesian OptimizationDensity EstimationImproving Environment Robustness of Deep Reinforcement Learning Approaches for Autonomous Racing Using Bayesian Optimization-based Curriculum Learning
Deep reinforcement learning (RL) approaches have been broadly applied to a large number of robotics tasks, such as robot manipulation and autonomous driving. However, an open problem in deep RL is learning policies that …
Autonomous DrivingAutonomous RacingBayesian OptimizationDeep Reinforcement Learning+4Probabilistic learning of the Purkinje network from the electrocardiogram
The identification of the Purkinje conduction system in the heart is a challenging task, yet essential for a correct definition of cardiac digital twins for precision cardiology. Here, we propose a probabilistic approach…
AnatomyBayesian OptimizationImproving search relevance of Azure Cognitive Search by Bayesian optimization
Azure Cognitive Search (ACS) has emerged as a major contender in "Search as a Service" cloud products in recent years. However, one of the major challenges for ACS users is to improve the relevance of the search results …
Bayesian OptimizationEvent-Triggered Safe Bayesian Optimization on Quadcopters
Bayesian optimization (BO) has proven to be a powerful tool for automatically tuning control parameters without requiring knowledge of the underlying system dynamics. Safe BO methods, in addition, guarantee safety during…
Bayesian Optimizationauto-sktime: Automated Time Series Forecasting
In today's data-driven landscape, time series forecasting is pivotal in decision-making across various sectors. Yet, the proliferation of more diverse time series data, coupled with the expanding landscape of available f…
AutoMLBayesian OptimizationDecision MakingTime Series+1Towards Safe Multi-Task Bayesian Optimization
Bayesian optimization has emerged as a highly effective tool for the safe online optimization of systems, due to its high sample efficiency and noise robustness. To further enhance its efficiency, reduced physical models…
Bayesian OptimizationGP+: A Python Library for Kernel-based learning via Gaussian Processes
In this paper we introduce GP+, an open-source library for kernel-based learning via Gaussian processes (GPs) which are powerful statistical models that are completely characterized by their parametric covariance and mea…
Bayesian OptimizationGaussian Processesparameter estimationCan LLMs Configure Software Tools
In software engineering, the meticulous configuration of software tools is crucial in ensuring optimal performance within intricate systems. However, the complexity inherent in selecting optimal configurations is exacerb…
Bayesian OptimizationHyperparameter OptimizationPhysics-Aware Multifidelity Bayesian Optimization: a Generalized Formulation
The adoption of high-fidelity models for many-query optimization problems is majorly limited by the significant computational cost required for their evaluation at every query. Multifidelity Bayesian methods (MFBO) allow…
Bayesian OptimizationManagementUsing Large Language Models for Hyperparameter Optimization
This paper explores the use of foundational large language models (LLMs) in hyperparameter optimization (HPO). Hyperparameters are critical in determining the effectiveness of machine learning models, yet their optimizat…
Bayesian OptimizationDecision MakingHyperparameter OptimizationConstrained Bayesian Optimization Under Partial Observations: Balanced Improvements and Provable Convergence
The partially observable constrained optimization problems (POCOPs) impede data-driven optimization techniques since an infeasible solution of POCOPs can provide little information about the objective as well as the cons…
Bayesian Optimization