Papers Bayesian Optimization
“Bayesian Optimization” 태그가 달린 논문 1,900편 · 필터 해제
High Dimensional Bayesian Optimization using Lasso Variable Selection
Bayesian optimization (BO) is a leading method for optimizing expensive black-box optimization and has been successfully applied across various scenarios. However, BO suffers from the curse of dimensionality, making it c…
Bayesian OptimizationComputational EfficiencyVariable SelectionMigrating a Job Search Relevance Function
In this paper, we describe the migration of a homebrewed C++ search engine to OpenSearch, aimed at preserving and improving search performance with minimal impact on business metrics. To facilitate the migration, we froz…
Bayesian OptimizationWhy risk matters for protein binder design
Bayesian optimization (BO) has recently become more prevalent in protein engineering applications and hence has become a fruitful target of benchmarks. However, current BO comparisons often overlook real-world considerat…
Bayesian OptimizationModel SelectionPortfolio OptimizationGraph-Eq: Discovering Mathematical Equations using Graph Generative Models
The ability to discover meaningful, accurate, and concise mathematical equations that describe datasets is valuable across various domains. Equations offer explicit relationships between variables, enabling deeper insigh…
Bayesian OptimizationEquation DiscoveryConfidence Adjusted Surprise Measure for Active Resourceful Trials (CA-SMART): A Data-driven Active Learning Framework for Accelerating Material Discovery under Resource Constraints
Accelerating the discovery and manufacturing of advanced materials with specific properties is a critical yet formidable challenge due to vast search space, high costs of experiments, and time-intensive nature of materia…
Active LearningBayesian Optimizationscientific discoveryAutomated and Risk-Aware Engine Control Calibration Using Constrained Bayesian Optimization
Decarbonization of the transport sector sets increasingly strict demands to maximize thermal efficiency and minimize greenhouse gas emissions of Internal Combustion Engines. This has led to complex engines with a surge i…
Bayesian OptimizationSelf-LearningWasserstein Distributionally Robust Bayesian Optimization with Continuous Context
We address the challenge of sequential data-driven decision-making under context distributional uncertainty. This problem arises in numerous real-world scenarios where the learner optimizes black-box objective functions …
Bayesian OptimizationDecision MakingData Mixture Optimization: A Multi-fidelity Multi-scale Bayesian Framework
Careful curation of data sources can significantly improve the performance of LLM pre-training, but predominant approaches rely heavily on intuition or costly trial-and-error, making them difficult to generalize across d…
Bayesian OptimizationSequential Decision MakingOptimal Parameter Adaptation for Safety-Critical Control via Safe Barrier Bayesian Optimization
Safety is of paramount importance in control systems to avoid costly risks and catastrophic damages. The control barrier function (CBF) method, a promising solution for safety-critical control, poses a new challenge of e…
Bayesian OptimizationBayesian Optimization of a Lightweight and Accurate Neural Network for Aerodynamic Performance Prediction
Ensuring high accuracy and efficiency of predictive models is paramount in the aerospace industry, particularly in the context of multidisciplinary design and optimization processes. These processes often require numerou…
Bayesian OptimizationCausal Bayesian Optimization with Unknown Graphs
Causal Bayesian Optimization (CBO) is a methodology designed to optimize an outcome variable by leveraging known causal relationships through targeted interventions. Traditional CBO methods require a fully and accurately…
Bayesian OptimizationA Framework for Finding Local Saddle Points in Two-Player Zero-Sum Black-Box Games
Saddle point optimization is a critical problem employed in numerous real-world applications, including portfolio optimization, generative adversarial networks, and robotics. It has been extensively studied in cases wher…
Bayesian OptimizationGaussian ProcessesPortfolio OptimizationBayesian Optimization for CVaR-based portfolio optimization
Optimal portfolio allocation is often formulated as a constrained risk problem, where one aims to minimize a risk measure subject to some performance constraints. This paper presents new Bayesian Optimization algorithms …
Bayesian OptimizationPortfolio OptimizationMachine Learning - Driven Materials Discovery: Unlocking Next-Generation Functional Materials -- A minireview
The rapid advancement of machine learning and artificial intelligence (AI)-driven techniques is revolutionizing materials discovery, property prediction, and material design by minimizing human intervention and accelerat…
AutoMLBayesian OptimizationFeature EngineeringModel Selection+1Robustness of deep learning classification to adversarial input on GPUs: asynchronous parallel accumulation is a source of vulnerability
The ability of machine learning (ML) classification models to resist small, targeted input perturbations - known as adversarial attacks - is a key measure of their safety and reliability. We show that floating-point non …
Adversarial RobustnessBayesian OptimizationGPUGraph Neural NetworkHAPI: A Model for Learning Robot Facial Expressions from Human Preferences
Automatic robotic facial expression generation is crucial for human-robot interaction, as handcrafted methods based on fixed joint configurations often yield rigid and unnatural behaviors. Although recent automated techn…
Bayesian OptimizationFacial expression generationLearning-To-RankPreferential Multi-Objective Bayesian Optimization for Drug Discovery
Despite decades of advancements in automated ligand screening, large-scale drug discovery remains resource-intensive and requires post-processing hit selection, a step where chemists manually select a few promising molec…
Bayesian OptimizationDrug DiscoveryExploiting Prior Knowledge in Preferential Learning of Individualized Autonomous Vehicle Driving Styles
Trajectory planning for automated vehicles commonly employs optimization over a moving horizon - Model Predictive Control - where the cost function critically influences the resulting driving style. However, finding a su…
Bayesian OptimizationModel Predictive ControlTrajectory PlanningAligning Crowd-sourced Human Feedback for Reinforcement Learning on Code Generation by Large Language Models
This paper studies how AI-assisted programming and large language models (LLM) improve software developers' ability via AI tools (LLM agents) like Github Copilot and Amazon CodeWhisperer, while integrating human feedback…
Bayesian OptimizationCode GenerationLanguage ModelingLanguage Modelling+2Safe exploration in reproducing kernel Hilbert spaces
Popular safe Bayesian optimization (BO) algorithms learn control policies for safety-critical systems in unknown environments. However, most algorithms make a smoothness assumption, which is encoded by a known bounded no…
Bayesian OptimizationSafe Exploration