10 citations · 17 across the 16 of their papers we have counts for
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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.LG2025
Busemann Functions in the Wasserstein Space: Existence, Closed-Forms, and Applications to Slicing
Clément Bonet, Elsa Cazelles, Lucas Drumetz +1
The Busemann function has recently found much interest in a variety of geometric machine learning problems, as it naturally defines projections onto geodesic rays of Riemannian man…
cs.LG2022★ 1 cited
Computationally-efficient initialisation of GPs: The generalised variogram method
Felipe Tobar, Elsa Cazelles, Taco de Wolff
We present a computationally-efficient strategy to initialise the hyperparameters of a Gaussian process (GP) avoiding the computation of the likelihood function. Our strategy can b…