Papers Bayesian Optimization
“Bayesian Optimization” 태그가 달린 논문 1,900편 · 필터 해제
Machine Learning Driven Sensitivity Analysis of E3SM Land Model Parameters for Wetland Methane Emissions
Methane (CH4) is the second most critical greenhouse gas after carbon dioxide, contributing to 16-25% of the observed atmospheric warming. Wetlands are the primary natural source of methane emissions globally. However, w…
Bayesian OptimizationCPUSensitivityRisk-Controlling Model Selection via Guided Bayesian Optimization
Adjustable hyperparameters of machine learning models typically impact various key trade-offs such as accuracy, fairness, robustness, or inference cost. Our goal in this paper is to find a configuration that adheres to u…
Bayesian OptimizationFairnessModel SelectionOptimal Data Generation in Multi-Dimensional Parameter Spaces, using Bayesian Optimization
Acquiring a substantial number of data points for training accurate machine learning (ML) models is a big challenge in scientific fields where data collection is resource-intensive. Here, we propose a novel approach for …
Bayesian OptimizationGPRRegret Optimality of GP-UCB
Gaussian Process Upper Confidence Bound (GP-UCB) is one of the most popular methods for optimizing black-box functions with noisy observations, due to its simple structure and superior performance. Its empirical successe…
Bayesian OptimizationScalable Meta-Learning with Gaussian Processes
Meta-learning is a powerful approach that exploits historical data to quickly solve new tasks from the same distribution. In the low-data regime, methods based on the closed-form posterior of Gaussian processes (GP) toge…
Bayesian OptimizationGaussian ProcessesMeta-LearningA Bayesian approach for prompt optimization in pre-trained language models
A prompt is a sequence of symbol or tokens, selected from a vocabulary according to some rule, which is prepended/concatenated to a textual query. A key problem is how to select the sequence of tokens: in this paper we f…
Bayesian OptimizationCombinatorial OptimizationLanguage ModellingLarge Language ModelPractical Path-based Bayesian Optimization
There has been a surge in interest in data-driven experimental design with applications to chemical engineering and drug manufacturing. Bayesian optimization (BO) has proven to be adaptable to such cases, since we can mo…
Bayesian OptimizationExperimental DesignAutomated discovery of trade-off between utility, privacy and fairness in machine learning models
Machine learning models are deployed as a central component in decision making and policy operations with direct impact on individuals' lives. In order to act ethically and comply with government regulations, these model…
Bayesian OptimizationDecision MakingFairnessDomain Knowledge Injection in Bayesian Search for New Materials
In this paper we propose DKIBO, a Bayesian optimization (BO) algorithm that accommodates domain knowledge to tune exploration in the search space. Bayesian optimization has recently emerged as a sample-efficient optimize…
Bayesian OptimizationControllable Expensive Multi-objective Learning with Warm-starting Bayesian Optimization
Pareto Set Learning (PSL) is a promising approach for approximating the entire Pareto front in multi-objective optimization (MOO) problems. However, existing derivative-free PSL methods are often unstable and inefficient…
Bayesian OptimizationGaussian ProcessesA General Framework for User-Guided Bayesian Optimization
The optimization of expensive-to-evaluate black-box functions is prevalent in various scientific disciplines. Bayesian optimization is an automatic, general and sample-efficient method to solve these problems with minima…
Bayesian OptimizationMulti-Objective Bayesian Optimization with Active Preference Learning
There are a lot of real-world black-box optimization problems that need to optimize multiple criteria simultaneously. However, in a multi-objective optimization (MOO) problem, identifying the whole Pareto front requires …
Active LearningBayesian OptimizationMulti-fidelity Bayesian Optimization in Engineering Design
Resided at the intersection of multi-fidelity optimization (MFO) and Bayesian optimization (BO), MF BO has found a niche in solving expensive engineering design optimization problems, thanks to its advantages in incorpor…
Bayesian OptimizationLABCAT: Locally adaptive Bayesian optimization using principal-component-aligned trust regions
Bayesian optimization (BO) is a popular method for optimizing expensive black-box functions. BO has several well-documented shortcomings, including computational slowdown with longer optimization runs, poor suitability f…
Bayesian OptimizationBenchmarkingBOIS: Bayesian Optimization of Interconnected Systems
Bayesian optimization (BO) has proven to be an effective paradigm for the global optimization of expensive-to-sample systems. One of the main advantages of BO is its use of Gaussian processes (GPs) to characterize model …
Bayesian OptimizationChemical ProcessGaussian Processesglobal-optimizationAccelerating material discovery with a threshold-driven hybrid acquisition policy-based Bayesian optimization
Advancements in materials play a crucial role in technological progress. However, the process of discovering and developing materials with desired properties is often impeded by substantial experimental costs, extensive …
Bayesian OptimizationData-Driven Moving Horizon Estimation Using Bayesian Optimization
In this work, an innovative data-driven moving horizon state estimation is proposed for model dynamic-unknown systems based on Bayesian optimization. As long as the measurement data is received, a locally linear dynamics…
Bayesian OptimizationState EstimationHigh-dimensional mixed-categorical Gaussian processes with application to multidisciplinary design optimization for a green aircraft
Recently, there has been a growing interest in mixed-categorical metamodels based on Gaussian Process (GP) for Bayesian optimization. In this context, different approaches can be used to build the mixed-categorical GP. M…
Bayesian OptimizationCantilever BeamDimensionality ReductionGaussian ProcessesOptimal simulation-based Bayesian decisions
We present a framework for the efficient computation of optimal Bayesian decisions under intractable likelihoods, by learning a surrogate model for the expected utility (or its distribution) as a function of the action a…
Active LearningBayesian OptimizationDecision MakingPosterior Sampling-Based Bayesian Optimization with Tighter Bayesian Regret Bounds
Among various acquisition functions (AFs) in Bayesian optimization (BO), Gaussian process upper confidence bound (GP-UCB) and Thompson sampling (TS) are well-known options with established theoretical properties regardin…
Bayesian OptimizationThompson Sampling