Papers Bayesian Optimization
“Bayesian Optimization” 태그가 달린 논문 1,900편 · 필터 해제
Reverse Attitude Statistics Based Star Map Identification Method
The star tracker is generally affected by the atmospheric background light and the aerodynamic environment when working in near space, which results in missing stars or false stars. Moreover, high-speed maneuvering may c…
Bayesian OptimizationPositionRobust Gaussian Processes via Relevance Pursuit
Gaussian processes (GPs) are non-parametric probabilistic regression models that are popular due to their flexibility, data efficiency, and well-calibrated uncertainty estimates. However, standard GP models assume homosk…
Bayesian OptimizationGaussian ProcessesregressionEARL-BO: Reinforcement Learning for Multi-Step Lookahead, High-Dimensional Bayesian Optimization
Conventional methods for Bayesian optimization (BO) primarily involve one-step optimal decisions (e.g., maximizing expected improvement of the next step). To avoid myopic behavior, multi-step lookahead BO algorithms such…
Bayesian OptimizationDecision MakingHyperparameter OptimizationReinforcement Learning (RL)+1HiBO: Hierarchical Bayesian Optimization via Adaptive Search Space Partitioning
Optimizing black-box functions in high-dimensional search spaces has been known to be challenging for traditional Bayesian Optimization (BO). In this paper, we introduce HiBO, a novel hierarchical algorithm integrating g…
Bayesian OptimizationManagementMulti-fidelity Machine Learning for Uncertainty Quantification and Optimization
In system analysis and design optimization, multiple computational models are typically available to represent a given physical system. These models can be broadly classified as high-fidelity models, which provide highly…
Bayesian OptimizationUncertainty QuantificationBayesian Optimization for Hyperparameters Tuning in Neural Networks
This study investigates the application of Bayesian Optimization (BO) for the hyperparameter tuning of neural networks, specifically targeting the enhancement of Convolutional Neural Networks (CNN) for image classificati…
Bayesian OptimizationComputational Efficiencyglobal-optimizationimage-classification+1Optimizing Posterior Samples for Bayesian Optimization via Rootfinding
Bayesian optimization devolves the global optimization of a costly objective function to the global optimization of a sequence of acquisition functions. This inner-loop optimization can be catastrophically difficult if i…
Bayesian Optimizationglobal-optimizationThompson SamplingSequential Large Language Model-Based Hyper-parameter Optimization
This study introduces SLLMBO, an innovative framework leveraging large language models (LLMs) for hyperparameter optimization (HPO), incorporating dynamic search space adaptability, enhanced parameter space exploitation,…
Bayesian OptimizationBenchmarkingHyperparameter Optimizationimage-classification+6Bayesian optimization for robust robotic grasping using a sensorized compliant hand
One of the first tasks we learn as children is to grasp objects based on our tactile perception. Incorporating such skill in robots will enable multiple applications, such as increasing flexibility in industrial processe…
Active LearningBayesian OptimizationRobotic GraspingGlobal Optimization of Gaussian Process Acquisition Functions Using a Piecewise-Linear Kernel Approximation
Bayesian optimization relies on iteratively constructing and optimizing an acquisition function. The latter turns out to be a challenging, non-convex optimization problem itself. Despite the relative importance of this s…
Bayesian Optimizationglobal-optimizationNear-Optimal Algorithm for Non-Stationary Kernelized Bandits
This paper studies a non-stationary kernelized bandit (KB) problem, also called time-varying Bayesian optimization, where one seeks to minimize the regret under an unknown reward function that varies over time. In partic…
Bayesian OptimizationDistributed Thompson sampling under constrained communication
In Bayesian optimization, a black-box function is maximized via the use of a surrogate model. We apply distributed Thompson sampling, using a Gaussian process as a surrogate model, to approach the multi-agent Bayesian op…
Bayesian OptimizationThompson SamplingUnleashing the Potential of Multi-Channel Fusion in Retrieval for Personalized Recommendations
Recommender systems (RS) are pivotal in managing information overload in modern digital services. A key challenge in RS is efficiently processing vast item pools to deliver highly personalized recommendations under stric…
Bayesian OptimizationRecommendation SystemsRetrievalFoundations of Adaptive High-Level Tight Control of Prostate Cancer: A Path from From Terminal Disease to Chronic Condition
Metastatic prostate cancer is one of the leading causes of cancer-related morbidity and mortality worldwide. It is characterized by a high mortality rate and a poor prognosis. In this work, we explore how a clinical onco…
Bayesian OptimizationPrognosisAmortized Probabilistic Conditioning for Optimization, Simulation and Inference
Amortized meta-learning methods based on pre-training have propelled fields like natural language processing and vision. Transformer-based neural processes and their variants are leading models for probabilistic meta-lea…
Bayesian OptimizationMeta-LearningTransferable Multi-Fidelity Bayesian Optimization for Radio Resource Management
Radio resource allocation often calls for the optimization of black-box objective functions whose evaluation is expensive in real-world deployments. Conventional optimization methods apply separately to each new system c…
Bayesian OptimizationManagementTesting the Efficacy of Hyperparameter Optimization Algorithms in Short-Term Load Forecasting
Accurate forecasting of electrical demand is essential for maintaining a stable and reliable power grid, optimizing the allocation of energy resources, and promoting efficient energy consumption practices. This study inv…
Bayesian OptimizationHyperparameter OptimizationLoad ForecastingA Comparative Analysis of Nigeria's Power Sector with and without Grid-Scale Storage: Future Implications for Emission and Renewable Energy Integration
This research proposes a framework for modeling and comparing two electricity scenarios for Nigeria by 2050, focusing on the inclusion and exclusion of electricity storage technologies. A Central Composite Design (CCD) w…
Bayesian OptimizationA Hybrid Feature Fusion Deep Learning Framework for Leukemia Cancer Detection in Microscopic Blood Sample Using Gated Recurrent Unit and Uncertainty Quantification
Acute lymphoblastic leukemia (ALL) is the most malignant form of leukemia and the most common cancer in adults and children. Traditionally, leukemia is diagnosed by analyzing blood and bone marrow smears under a microsco…
AllBayesian Optimizationimage-classificationImage Classification+1The Traveling Bandit: A Framework for Bayesian Optimization with Movement Costs
This paper introduces a framework for Bayesian Optimization (BO) with metric movement costs, addressing a critical challenge in practical applications where input alterations incur varying costs. Our approach is a conven…
Bayesian OptimizationTraveling Salesman Problem