Gaussian process probabilistic programming (GPPP) is a term I've coined for the work I'm doing to re-design the way that we work with GPs in a practical sense. I presented the high-level aspects of this work at ProbProg and JuliaCon, and continue to work on a paper.
Implementations available in Julia and Python.
GPAR is a multi-output Gaussian process-based regression model. It's simple, scalable by GP standards, and seems to work really well in practice. In particular it overcomes some of the limitations of standard multi-output GPs. It appeared at AISTATS 2019.
Implementations available in Python and Julia.
The OILMM is another multi-output Gaussian process model for regression that's easily able to handle a lot of outputs while retaining exact inference. It's really Wessel's thing – he came up with the main idea, and he and Eric did most of the work. I pointed out the connection with separable spatio-temporal processes and state space approximations, some ideas for experiments, and used TemporalGPs.jl to run one of the experiments. It appeared at ICML 2020.
Implementations available in Python and Julia.
Mooncake.jl is an AD for the Julia language that Hong and I have created. It is written entirely in the Julia programming language, while supporting much more of the language and being much more performant than any previous AD written in this way.
This is a Julia implementation of my GPPP work. It's permanently ongoing, but makes working with GPs in problems involving multiple related processes signficantly more straightforward than traditional GP packages.
JuliaCon 2019: talk and slides.
This work implements SDE approximations to GPs, which dramatically accelerates inference and learning for models involving long time horizons. This is notable because the standard pseudo-point approximations fail in these scenarios.
JuliaCon 2020: talk and slides.
The above two packages have been incorporated into the JuliaGPs organisation, which aims to unify a range of packages for GPs in the Julia ecosystem, and to provide strong foundations for research into GPs, and their use in practice.
JuliaCon 2022: talk