Scalable Bayesian Optimization with Sparse Gaussian Process Models

2020/10/26 by Ang Yang, Yang, Ang
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Advanced Multi-Objective Optimization Algorithms #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Machine Learning (stat.ML)

paper · pdf · doi:10.48550/arxiv.2010.13301

openalex publication_date 2020/10/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This thesis focuses on Bayesian optimization with the improvements coming from two aspects:(i) the use of derivative information to accelerate the optimization convergence; and (ii) the consideration of scalable GPs for handling massive data.

Citations

• Generalized Product of Experts for Automatic and Principled Fusion of Gaussian Process Predictions
• Lipschitzian optimization without the Lipschitz constant
• Bandit Theory meets Compressed Sensing for high dimensional Stochastic Linear Bandit
• On Estimation of a Probability Density Function and Mode
• Practical Bayesian Optimization of Machine Learning Algorithms
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• Exploiting gradients and Hessians in Bayesian optimization and Bayesian quadrature
• D-VAE: A Variational Autoencoder for Directed Acyclic Graphs
• Variational Inference: A Review for Statisticians

Related

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• Relaxed Gaussian process interpolation: a goal-oriented approach to Bayesian optimization