Paper accepted at KDD 2021!

Latent variable models are powerful statistical tools that can uncover relevant variation between patients or cells, by inferring unobserved hidden states from observable high-dimensional data. A major shortcoming of current methods, however, is their inability to learn sparse and interpretable hidden states.

Paper accepted at UAI!

Paper accepted! Our latest work on multi-output Gaussian Process Latent Variable models got accepted at this years’s UAI! Check out the preprint here.