Papers Bayesian Optimization
“Bayesian Optimization” 태그가 달린 논문 1,900편 · 필터 해제
Transductive Active Learning: Theory and Applications
We study a generalization of classical active learning to real-world settings with concrete prediction targets where sampling is restricted to an accessible region of the domain, while prediction targets may lie outside …
Active LearningBayesian OptimizationLearning TheoryPrediction+1Boundary Exploration for Bayesian Optimization With Unknown Physical Constraints
Bayesian optimization has been successfully applied to optimize black-box functions where the number of evaluations is severely limited. However, in many real-world applications, it is hard or impossible to know in advan…
Bayesian OptimizationGaussian ProcessesNoise-Adaptive Confidence Sets for Linear Bandits and Application to Bayesian Optimization
Adapting to a priori unknown noise level is a very important but challenging problem in sequential decision-making as efficient exploration typically requires knowledge of the noise level, which is often loosely specifie…
Bayesian OptimizationDecision MakingEfficient ExplorationSequential Decision MakingGenerative Adversarial Model-Based Optimization via Source Critic Regularization
Offline model-based optimization seeks to optimize against a learned surrogate model without querying the true oracle objective function during optimization. Such tasks are commonly encountered in protein design, robotic…
Bayesian OptimizationProtein DesignCURE: Simulation-Augmented Auto-Tuning in Robotics
Robotic systems are typically composed of various subsystems, such as localization and navigation, each encompassing numerous configurable components (e.g., selecting different planning algorithms). Once an algorithm has…
Bayesian OptimizationPrincipled Preferential Bayesian Optimization
We study the problem of preferential Bayesian optimization (BO), where we aim to optimize a black-box function with only preference feedback over a pair of candidate solutions. Inspired by the likelihood ratio idea, we c…
Bayesian OptimizationGaussian ProcessesA Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules?
Automation is one of the cornerstones of contemporary material discovery. Bayesian optimization (BO) is an essential part of such workflows, enabling scientists to leverage prior domain knowledge into efficient explorati…
Bayesian OptimizationEfficient ExplorationVoronoi Candidates for Bayesian Optimization
Bayesian optimization (BO) offers an elegant approach for efficiently optimizing black-box functions. However, acquisition criteria demand their own challenging inner-optimization, which can induce significant overhead. …
Bayesian OptimizationGaussian ProcessesAn Artificial Intelligence (AI) workflow for catalyst design and optimization
In the pursuit of novel catalyst development to address pressing environmental concerns and energy demand, conventional design and optimization methods often fall short due to the complexity and vastness of the catalyst …
Active LearningBayesian OptimizationLarge Language Models to Enhance Bayesian Optimization
Bayesian optimization (BO) is a powerful approach for optimizing complex and expensive-to-evaluate black-box functions. Its importance is underscored in many applications, notably including hyperparameter tuning, but its…
Bayesian OptimizationFew-Shot LearningFully autonomous tuning of a spin qubit
Spanning over two decades, the study of qubits in semiconductors for quantum computing has yielded significant breakthroughs. However, the development of large-scale semiconductor quantum circuits is still limited by cha…
Bayesian OptimizationPoisson Process for Bayesian Optimization
BayesianOptimization(BO) is a sample-efficient black-box optimizer, and extensive methods have been proposed to build the absolute function response of the black-box function through a probabilistic surrogate model, incl…
Bayesian OptimizationHyperparameter OptimizationNeural Architecture SearchSMAC+1High-dimensional Bayesian Optimization via Covariance Matrix Adaptation Strategy
Bayesian Optimization (BO) is an effective method for finding the global optimum of expensive black-box functions. However, it is well known that applying BO to high-dimensional optimization problems is challenging. To a…
Bayesian OptimizationStandard Gaussian Process Can Be Excellent for High-Dimensional Bayesian Optimization
A longstanding belief holds that Bayesian Optimization (BO) with standard Gaussian processes (GP) -- referred to as standard BO -- underperforms in high-dimensional optimization problems. While this belief seems plausibl…
Bayesian OptimizationGaussian ProcessesGenetic-guided GFlowNets for Sample Efficient Molecular Optimization
The challenge of discovering new molecules with desired properties is crucial in domains like drug discovery and material design. Recent advances in deep learning-based generative methods have shown promise but face the …
Bayesian OptimizationDrug DiscoveryA Safe Reinforcement Learning driven Weights-varying Model Predictive Control for Autonomous Vehicle Motion Control
Determining the optimal cost function parameters of Model Predictive Control (MPC) to optimize multiple control objectives is a challenging and time-consuming task. Multiobjective Bayesian Optimization (BO) techniques so…
Bayesian OptimizationDeep Reinforcement LearningModel Predictive ControlReinforcement Learning (RL)+1PROSAC: Provably Safe Certification for Machine Learning Models under Adversarial Attacks
It is widely known that state-of-the-art machine learning models, including vision and language models, can be seriously compromised by adversarial perturbations. It is therefore increasingly relevant to develop capabili…
Adversarial AttackBayesian OptimizationCausal Bayesian Optimization via Exogenous Distribution Learning
Maximizing a target variable as an operational objective in a structural causal model is an important problem. Existing Causal Bayesian Optimization~(CBO) methods either rely on hard interventions that alter the causal s…
Bayesian OptimizationAccelerating Look-ahead in Bayesian Optimization: Multilevel Monte Carlo is All you Need
We leverage multilevel Monte Carlo (MLMC) to improve the performance of multi-step look-ahead Bayesian optimization (BO) methods that involve nested expectations and maximizations. Often these expectations must be comput…
AllBayesian OptimizationCo-orchestration of Multiple Instruments to Uncover Structure-Property Relationships in Combinatorial Libraries
The rapid growth of automated and autonomous instrumentations brings forth an opportunity for the co-orchestration of multimodal tools, equipped with multiple sequential detection methods, or several characterization too…
Bayesian OptimizationDimensionality ReductionGaussian ProcessesRepresentation Learning