Active Learning of Linear Embeddings for Gaussian Processes
This addresses the practical difficulties in high-dimensional GP tasks, providing a solution for researchers and practitioners in machine learning and statistics.
The authors tackled the problem of discovering low-dimensional structure in high-dimensional Gaussian process tasks by proposing an active learning method, which also includes a technique for marginalizing GP hyperparameters to improve robustness, offering efficient GP regression, quadrature, or Bayesian optimization.
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.