Enhancing Bayesian Optimization and Active Learning Through Kernel Diversity
Hyperparameter selection remains a key challenge in Bayesian optimization (BO) and Bayesian active learning (AL), as model misspecification can lead to suboptimal performance, while more accurate fully Bayesian treatments typically rely on computationally expensive MCMC sampling. This paper proposes a unified framework, KENDO (Kernel ENsemble Disagreement-aware Operator), that integrates Ensemble Gaussian Processes (EGP) with disagreement-aware acquisition strategies. The central idea is to replace hyperparameter sampling with a kernel ensemble and adaptive Bayesian weighting, combined with di
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- PossiblePossibly related (embedding) · 45%Offline Reinforcement Learning Improves Through Active Model Selection and Bayesian Optimization - Bioengineer.org →
- FuzzySimilar title/name (fuzzy) · 59%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.73): “Enhancing Bayesian Optimization and Active Learning Through ” ≈ “aymericdamien/TopDeepLearning””
- FuzzySimilar title/name (fuzzy) · 59%microsoft/semantic-kernel →
“Fuzzy title match (0.73): “Enhancing Bayesian Optimization and Active Learning Through ” ≈ “microsoft/semantic-kernel””
- LinkedLinked via arxiv author · 85%Jiazheng Zhang →
“Enhancing Bayesian Optimization and Active Learning Through Kernel Diversity”
- LinkedLinked via arxiv author · 85%Haotian Xiang →
“Enhancing Bayesian Optimization and Active Learning Through Kernel Diversity”
- LinkedLinked via arxiv author · 85%Qin Lu →
“Enhancing Bayesian Optimization and Active Learning Through Kernel Diversity”
- LinkedLinked via arxiv author · 85%Konstantinos D. Polyzos →
“Enhancing Bayesian Optimization and Active Learning Through Kernel Diversity”
- LinkedLinked via arxiv author · 85%Tara Javidi →
“Enhancing Bayesian Optimization and Active Learning Through Kernel Diversity”