Vincent Adam: Doubly Sparse Variational Gaussian Processes

Bio

Vincent Adam studied engineering and cognitive science in France before completing a PhD in between these fields at the Gatsby Unit, UCL. There, he studied perception, framing it as a probabilistic inference problem, and using psychophysical methodology. He also developed the non-parametric regression methods he needed to analyze behavioral data. Since then, working at Prowler.io, his research has focused on deriving scalable, yet accurate, approximate inference algorithms (mainly variational) for models including Gaussian process priors.