Event

Lukasz Kidzinski, Stanford University

Monday, April 23, 2018 10:30to11:30
room 5340, Pav. André-Aisenstadt, 2920, ch. de la Tour, CA

Sparse longitudinal modeling using matrix factorization.

A common problem in clinical practice is to predict disease progression from sparse observations of individual patients. The classical approach to modeling this kind of data relies on a mixed-effect model where time is considered as both a fixed effect (a population trajectory) and a random effect (an individual trajectory). In our work, we map the problem to a matrix completion framework and solve it using matrix factorization techniques. The proposed approach does not require assumptions of the mixed-effect model and it can be naturally extended to multivariate measurements.

Monsieur Kidzinski est candidat pour un poste en apprentissage automatique au Département de mathématiques et de statistique.
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