Bayesian optimization with approximate set kernels

DOI10.1007/s10994-021-05949-0OpenAlexW3138674686MaRDI QIDQ2051286

Tackgeun You, Jungtaek Kim, Saehoon Kim, Seungjin Choi, Michael J. McCourt

Publication date: 24 November 2021

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1905.09780

zbMATH Keywords

global optimizationset optimizationBayesian optimization

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (2)

CMD: controllable matrix decomposition with global optimization for deep neural network compression ⋮ Reproducing kernel Hilbert spaces in the mean field limit

Uses Software

• L-BFGS
• UCI-ml
• Scikit
• Spearmint
• Hyperopt
• k-means++
• SMAC
• GaussQR
• Open3D

Cites Work

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• On the limited memory BFGS method for large scale optimization
• Lipschitzian optimization without the Lipschitz constant
• Kernel-based Approximation Methods using MATLAB
• 10.1162/15324430260185646
• Kernel Mean Embedding of Distributions: A Review and Beyond
• Least squares quantization in PCM

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