Gaussian process regression was designed for problems with strictly numeric predictor variables. However, GPR can be used with categorical predictor variables by using one-hot encoding.

Pantelis Samartsidis, Claudia R. Eickhoff, Simon B. Eickhoff, Tor D. Wager, Lisa Feldman Barrett, Shir Atzil, Timothy D. Johnson, Thomas E. Nichols, Bayesian log-Gaussian Cox process regression, ...

Bo Wang, Jian Qing Shi, Generalized Gaussian Process Regression Model for Non-Gaussian Functional Data, Journal of the American Statistical Association, Vol. 109, No. 507 (September 2014), pp.

Presentations "Localized Surface Plasmonic Catalysis and Local Minima Search Boosted by Gaussian Process," Molecular Interactions and Dynamics (MID) Gordon Research Conference, July 2022 ...

Machine learning hedge strategy with deep Gaussian process regression An optimal hedging strategy for options in discrete time using a reinforcement learning technique ...