Arno Solin

Hilbert space methods for reduced-rank Gaussian process regression

2019 · Statistics and Computing

This paper proposes a novel scheme for reduced-rank Gaussian process regression. The method is based on an approximate series expansion of the covariance function in terms of an eigenfunction expansion of the Laplace operator in …

Combining Pseudo-Point and State Space Approximationsfor Sum-Separable Gaussian Processes

2021 · Uncertainty in Artificial Intelligence

Spatio-temporal Gaussian processes (GPs) are important probabilistic tools for inference and learning in climate science, epidemiology, or any time-driven general GP modelling problem. The current gold-standard methods for scaling GPs to large data sets are …

Towards Improved Learning in Gaussian Processes: The Best of Two Worlds

2022 · arXiv (Cornell University)

Gaussian process training decomposes into inference of the (approximate) posterior and learning of the hyperparameters. For non-Gaussian (non-conjugate) likelihoods, two common choices for approximate inference are Expectation Propagation (EP) and Variational Inference (VI), which have …

Physics-Informed Variational State-Space Gaussian Processes

2024 · arXiv (Cornell University)

Differential equations are important mechanistic models that are integral to many scientific and engineering applications. With the abundance of available data there has been a growing interest in data-driven physics-informed models. Gaussian processes (GPs) are …