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arXiv AI By Heng Zhang, Haotian Xiang, Qin Lu, Konstantinos D. Polyzos, Tara Javidi

Enhancing Bayesian Optimization and Active Learning Through Kernel Diversity

Read the original on arXiv AI →

The paper introduces KENDO, a unified framework that combines Ensemble Gaussian Processes with disagreement‑aware acquisition strategies to address hyperparameter selection in Bayesian optimization and active learning. By replacing costly hyperparameter sampling with a kernel ensemble and adaptive Bayesian weighting, KENDO‑BO and KENDO‑AL provide self‑correcting mechanisms tailored to their respective tasks. Experiments on synthetic and real‑world benchmarks show that KENDO‑BO matches or outperforms state‑of‑the‑art methods while cutting computational cost up to fivefold, and KENDO‑AL delivers better predictive calibration with up to 27‑times speedup compared to MCMC‑based baselines.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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