Papers Bayesian Optimization
“Bayesian Optimization” 태그가 달린 논문 1,900편 · 필터 해제
Predicting from Strings: Language Model Embeddings for Bayesian Optimization
Bayesian Optimization is ubiquitous in the field of experimental design and blackbox optimization for improving search efficiency, but has been traditionally restricted to regression models which are only applicable to f…
Bayesian OptimizationExperimental DesignHyperparameter OptimizationLanguage Modeling+2Bayesian Optimisation with Unknown Hyperparameters: Regret Bounds Logarithmically Closer to Optimal
Bayesian Optimization (BO) is widely used for optimising black-box functions but requires us to specify the length scale hyperparameter, which defines the smoothness of the functions the optimizer will consider. Most cur…
Bayesian OptimisationBayesian OptimizationBatched Energy-Entropy acquisition for Bayesian Optimization
Bayesian optimization (BO) is an attractive machine learning framework for performing sample-efficient global optimization of black-box functions. The optimization process is guided by an acquisition function that select…
Bayesian OptimizationGaussian Processesglobal-optimizationRethinking Gradient-Based Methods: Multi-Property Materials Design Beyond Differentiable Targets
Gradient-based methods offer a simple, efficient strategy for materials design by directly optimizing candidates using gradients from pretrained property predictors. However, their use in crystal structure optimization i…
Band GapBayesian OptimizationProperty PredictionRanking over Regression for Bayesian Optimization and Molecule Selection
Bayesian optimization (BO) has become an indispensable tool for autonomous decision-making across diverse applications from autonomous vehicle control to accelerated drug and materials discovery. With the growing interes…
Bayesian OptimizationDecision MakingregressionGaussian Process Thompson Sampling via Rootfinding
Thompson sampling (TS) is a simple, effective stochastic policy in Bayesian decision making. It samples the posterior belief about the reward profile and optimizes the sample to obtain a candidate decision. In continuous…
Bayesian OptimizationDecision Makingglobal-optimizationThompson SamplingBatched Bayesian optimization by maximizing the probability of including the optimum
Batched Bayesian optimization (BO) can accelerate molecular design by efficiently identifying top-performing compounds from a large chemical library. Existing acquisition strategies for batch design in BO aim to balance …
Bayesian OptimizationDiversityThompson SamplingClosed-Loop phase selection in EEG-TMS using Bayesian Optimization
Research on transcranial magnetic stimulation (TMS) combined with encephalography feedback (EEG-TMS) has shown that the phase of the sensorimotor mu rhythm is predictive of corticospinal excitability. Thus, if the subjec…
Bayesian OptimizationEEGregressionRhythmSafe Learning-Based Optimization of Model Predictive Control: Application to Battery Fast-Charging
Model predictive control (MPC) is a powerful tool for controlling complex nonlinear systems under constraints, but often struggles with model uncertainties and the design of suitable cost functions. To address these chal…
Bayesian OptimizationModel Predictive ControlMechanistic Behavior Editing of Language Models
Large Language Models trained on web-scale text acquire language generation abilities that can solve a wide range of tasks, particularly when task knowledge is refined into the generative prior using in-context examples.…
Bayesian OptimizationSpecificityText GenerationRapid optimization in high dimensional space by deep kernel learning augmented genetic algorithms
Exploration of complex high-dimensional spaces presents significant challenges in fields such as molecular discovery, process optimization, and supply chain management. Genetic Algorithms (GAs), while offering significan…
Bayesian OptimizationManagementMeasurements with Noise: Bayesian Optimization for Co-optimizing Noise and Property Discovery in Automated Experiments
We have developed a Bayesian optimization (BO) workflow that integrates intra-step noise optimization into automated experimental cycles. Traditional BO approaches in automated experiments focus on optimizing experimenta…
Bayesian OptimizationExpected Diverse Utility (EDU): Diverse Bayesian Optimization of Expensive Computer Simulators
The optimization of expensive black-box simulators arises in a myriad of modern scientific and engineering applications. Bayesian optimization provides an appealing solution, by leveraging a fitted surrogate model to gui…
Bayesian OptimizationDiversityBest Practices for Multi-Fidelity Bayesian Optimization in Materials and Molecular Research
Multi-fidelity Bayesian Optimization (MFBO) is a promising framework to speed up materials and molecular discovery as sources of information of different accuracies are at hand at increasing cost. Despite its potential u…
Bayesian OptimizationInformativenessOptimistic Games for Combinatorial Bayesian Optimization with Application to Protein Design
Bayesian optimization (BO) is a powerful framework to optimize black-box expensive-to-evaluate functions via sequential interactions. In several important problems (e.g. drug discovery, circuit design, neural architectur…
Bayesian OptimizationDrug DiscoveryNeural Architecture SearchProtein DesignSafe Time-Varying Optimization based on Gaussian Processes with Spatio-Temporal Kernel
Ensuring safety is a key aspect in sequential decision making problems, such as robotics or process control. The complexity of the underlying systems often makes finding the optimal decision challenging, especially when …
Bayesian OptimizationChange DetectionDecision MakingGaussian Processes+1Jump Diffusion-Informed Neural Networks with Transfer Learning for Accurate American Option Pricing under Data Scarcity
Option pricing models, essential in financial mathematics and risk management, have been extensively studied and recently advanced by AI methodologies. However, American option pricing remains challenging due to the comp…
Bayesian OptimizationData AugmentationTransfer LearningAUGUR, A flexible and efficient optimization algorithm for identification of optimal adsorption sites
In this paper, we propose a novel flexible optimization pipeline for determining the optimal adsorption sites, named AUGUR (Aware of Uncertainty Graph Unit Regression). Our model combines graph neural networks and Gaussi…
Bayesian OptimizationGaussian ProcessesUncertainty QuantificationSupply Risk-Aware Alloy Discovery and Design
Materials design is a critical driver of innovation, yet overlooking the technological, economic, and environmental risks inherent in materials and their supply chains can lead to unsustainable and risk-prone solutions. …
Bayesian OptimizationNavigateSwine Diet Design using Multi-objective Regionalized Bayesian Optimization
The design of food diets in the context of animal nutrition is a complex problem that aims to develop cost-effective formulations while balancing minimum nutritional content. Traditional approaches based on theoretical m…
Bayesian OptimizationNutrition