Paper accepted! Our latest work on developing a versatile framework for rapid prototyping and training of a wide range of factor analysis models for multi-omics data got accepted at this year’s ICML workshop on computational biology!
Paper accepted! Our latest work on a general bias-variance decomposition for proper scores got accepted at this year’s AISTATS conference!
Paper accepted! Our latest work on multi-view latent variable models with structured sparsity got accepted at this year’s AISTATS conference!
Paper accepted! Our latest work on quantifying uncertainty under real-world conditions got accepted at this year’s AAAI conference on artificial intelligence!
Paper accepted! Our latest work on boosting the expressive power in post-hoc uncertainty calibration got accepted at this year’s ECCV!
Paper accepted! Our latest work on characterizing proteogenomic subtypes of AML got accepted at this year’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 year’s UAI!