Scalable Log Determinants for Gaussian Process Kernel Learning

@inproceedings{Dong2017ScalableLD,
  title={Scalable Log Determinants for Gaussian Process Kernel Learning},
  author={Kun Dong and David Eriksson and Hannes Nickisch and David Bindel and Andrew Gordon Wilson},
  booktitle={NIPS},
  year={2017}
}
For applications as varied as Bayesian neural networks, determinantal point processes, elliptical graphical models, and kernel learning for Gaussian processes (GPs), one must compute a log determinant of an n× n positive definite matrix, and its derivatives – leading to prohibitive O(n) computations. We propose novel O(n) approaches to estimating these quantities from only fast matrix vector multiplications (MVMs). These stochastic approximations are based on Chebyshev, Lanczos, and surrogate… CONTINUE READING
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