Paper accepted! Our latest work on boosting the expressive power in post-hoc uncertainty calibration got accepted at this years’s ECCV!
Paper accepted! Our latest work on multi-view latent variable models with structured sparsity got accepted at this years’s ICML workshop on interpretable ML in Healthcare!
Paper accepted! Our latest work on characterizing proteogenomic subtypes of AML got accepted at this years’s Cancer Cell!
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! Our latest work on multi-output Gaussian Process Latent Variable models got accepted at this years’s UAI!