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cs.LG2026
Accelerated training of Gaussian processes using banded square exponential covariances
Emily C. Ehrhardt, Felipe Tobar
We propose a novel approach to computationally efficient GP training based on the observation that square-exponential (SE) covariance matrices contain several off-diagonal entries…
cs.LG2026
Efficient Gaussian process learning via subspace projections
Elsa Cazelles, Felipe Tobar
We propose a novel training objective for GPs constructed using lower-dimensional linear projections of the data, referred to as \emph{projected likelihood} (PL). We provide a clos…
cs.LG2023
Asynchronous Graph Generator
Christopher P. Ley, Felipe Tobar
We introduce the asynchronous graph generator (AGG), a novel graph attention network for imputation and prediction of multi-channel time series. Free from recurrent components or a…