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UID:55@cran.univ-lorraine.fr
DTSTART;TZID=Europe/Paris:20240119T150000
DTEND;TZID=Europe/Paris:20240119T160000
DTSTAMP:20240316T155639Z
URL:https://www.cran.univ-lorraine.fr/events/seminaire-projet-simul-biosis
 -8/
SUMMARY:Séminaire Shuyu Dong
DESCRIPTION:Matrix and tensor decomposition plays a crucial role in address
 ing various real-world problems related to topics such as statistical infe
 rence\, data acquisition\, and data restoration. In this talk\, we start w
 ith low-rank matrix/tensor models for the data completion problem [1\,2]. 
 We tackle this problem in the framework of low-rank matrix/tensor decompos
 ition with a least-squares model. These rank-constrained problems are know
 n not only for their low computational complexity but also the capability 
 of extracting the most important information in the data. We discuss a typ
 e of Riemannian gradient-based algorithms that exploit the structure of th
 ese rank-constrained models. Secondly\, we present a novel application of 
 low-rank matrix methods in the context of causal structure learning. We wi
 ll show how low-rank matrix decomposition\, in combination with a sparse m
 ask operator\, can be used to efficiently find directed acyclic graphs (DA
 Gs) proximal to a given graph (with cycles). Furthermore\, for learning ca
 usal DAGs from observational data\, we present a sparse matrix decompositi
 on method [4] and discuss its efficiency through experiments on synthetic 
 and real-world data.\n[1] S. Dong\, P.-A. Absil\, and K. A. Gallivan\, Rie
 mannian gradient descent methods for graph-regularized matrix completion. 
 Linear Algebra and its Applications 623 (2021)\, 193-235\n[2] S. Dong\, B.
  Gao\, Y. Guan\, and F. Glineur\, New Riemannian preconditioned algorithms
  for tensor completion via polyadic decomposition\, SIAM Journal on Matrix
  Analysis and Applications 43 (2) (2022)\, 840-866\n[3] S. Dong and M. Seb
 ag\, From graphs to DAGs: a low-complexity model and a scalable algorithm\
 , European Conference on Machine Learning and Principles and Practice of K
 nowledge Discovery in Databases (ECML-PKDD)\, 2022\n[4] S. Dong\, K. Uemur
 a\, A. Fujii\, S. Chang\, Y. Koyanagi\, K. Maruhashi\, and M. Sebag\, Lear
 ning large causal structures from inverse covariance matrix via matrix dec
 omposition\, arXiv preprint arXiv:2211.14221\, 2023
CATEGORIES:Département BioSiS,Séminaires projet SiMul
LOCATION:CRAN - FST - 4ème\, Campus Sciences\, Boulevard des Aiguillettes\
 , Vandoeuvre-lès-Nancy\, 54506\, France
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Campus Sciences\, Boulevard
  des Aiguillettes\, Vandoeuvre-lès-Nancy\, 54506\, France;X-APPLE-RADIUS=
 100;X-TITLE=CRAN - FST - 4ème:geo:0,0
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