Active Learning of Linear Embeddings for Gaussian Processes

Abstract

We propose an active learning method for discovering low-dimensional structure in high-dimensional Gaussian process (GP) tasks. Such problems are increasingly frequent and important, but have hitherto presented severe practical difficulties. We further introduce a novel technique for approximately marginalizing GP hyperparameters, yielding marginal predictions robust to hyperparameter mis-specification. Our method offers an efficient means of performing GP regression, quadrature, or Bayesian optimization in high-dimensional spaces.

References in corpus (5)

• A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning
• Bayesian Active Learning for Classification and Preference Learning
• Joint Optimization and Variable Selection of High-dimensional Gaussian Processes
• Bayesian Optimization in a Billion Dimensions via Random Embeddings
• Bandit Theory meets Compressed Sensing for high dimensional Stochastic Linear Bandit

Cited by in corpus (8)

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• ENTMOOT: A Framework for Optimization over Ensemble Tree Models
• Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining
• Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian Processes
• Trading Convergence Rate with Computational Budget in High Dimensional Bayesian Optimization
• The Differentiable Cross-Entropy Method
• Parameter Optimization using high-dimensional Bayesian Optimization
• Global Optimisation of Black-Box Functions with Generative Models in the Wasserstein Space