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UID:20250725T213153EDT-4327dkJw7O@132.216.98.100
DTSTAMP:20250726T013153Z
DESCRIPTION:Sparse longitudinal modeling using matrix factorization.\n\nA c
 ommon problem in clinical practice is to predict disease progression from 
 sparse observations of individual patients. The classical approach to mode
 ling this kind of data relies on a mixed-effect model where time is consid
 ered 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 c
 ompletion framework and solve it using matrix factorization techniques. Th
 e proposed approach does not require assumptions of the mixed-effect model
  and it can be naturally extended to multivariate measurements.\n	\n	Monsieu
 r Kidzinski est candidat pour un poste en apprentissage automatique au Dép
 artement de mathématiques et de statistique.\n
DTSTART:20180423T143000Z
DTEND:20180423T153000Z
LOCATION:room 5340\, CA\, Pav. André-Aisenstadt\, 2920\, ch. de la Tour
SUMMARY:Lukasz Kidzinski\, Stanford University
URL:/mathstat/channels/event/lukasz-kidzinski-stanford
 -university-286736
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