deepgp Package

Maintainer: Annie Sauer anniees@vt.edu

Performs posterior inference for deep Gaussian processes following Sauer, Gramacy, and Higdon (2020) <arXiv:2012.08015>. Models are trained through MCMC including elliptical slice sampling of latent Gaussian layers and Metropolis-Hastings sampling of kernel hyperparameters. Vecchia-approximation for faster computation is implemented following Sauer, Cooper, and Gramacy (2022) <arXiv:2204.02904>. Downstream tasks include sequential design through active learning Cohn/integrated mean squared error (ALC/IMSE; Sauer, Gramacy, and Higdon, 2020) and optimization through expected improvement (EI; Gramacy, Sauer, and Wycoff, 2021 <arXiv:2112.07457>). Models extend up to three layers deep; a one layer model is equivalent to typical Gaussian process regression. Covariance kernel options are matern (default) and squared exponential. Applicable to both noisy and deterministic functions. Incorporates SNOW parallelization; utilizes C and C++ with OpenMP parallelization under the hood.

Run help("deepgp-package") or help(package = "deepgp") for more information.

References

Sauer, A., Gramacy, R.B., & Higdon, D. (2020). Active learning for deep Gaussian process surrogates. Technometrics, (just-accepted), 1-39.

Sauer, A., Cooper, A., & Gramacy, R. B. (2022). Vecchia-approximated deep Gaussian processes for computer experiments. pre-print on arXiv:2204.02904

Version History

What’s new in version 1.0.1?

• Minor bug fixes/improvements.
• New warning message when OpenMP parallelization is not utilized for the Vecchia approximation. This happens when the package is downloaded from CRAN on a Mac. To set up OpenMP, download package source and compile with gcc/g++ instead of clang.

What’s new in version 1.0.0?

• Models may now leverage the Vecchia approximation (through the specification of vecchia = TRUE in fit functions) for faster computation. The speed of this implementation relies on OpenMP parallelization (make sure the -fopenmp flag is present with package installation).
• SNOW parallelization now uses less memory/storage.
• tau2 is now calculated at the time of MCMC, not at the time of prediction. This avoids some extra calculations.

What’s new in version 0.3.0?

• The Matern kernel is now the default covariance. The smoothness parameter is user-adjustable but must be either v = 0.5, v = 1.5, or v = 2.5 (default). The squared exponential kernel is still required for use with ALC and IMSE (set cov = "exp2" in fit functions).
• Expected improvement (EI) may now be computed concurrently with predictions. Set EI = TRUE inside predict calls. EI calculations are nugget-free and are for minimizing the response (negate y if maximization is desired).
• To save memory, hidden layer mappings used in predictions are no longer stored and returned by default. To store them, set store_latent = TRUE inside predict.