My CAMSAP 2019 Schedule

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TA1: Large Random Matrix Theory in Signal Processing and Machine Learning

Session Type: Lecture
Time: Tuesday, December 17, 17:45 - 19:45
Location: Salle Fort Royal
Session Chair: Nicolas Gillis, University of Mons
 
  TA1.1: PHASE TRANSITIONS IN THE DYNAMIC MODE DECOMPOSITION ALGORITHM
         Arvind Prasadan; University of Michigan
         Asad Lodhia; University of Michigan
         Raj Rao Nadakuditi; University of Michigan
 
  TA1.2: A RANDOM MATRIX ANALYSIS AND OPTIMIZATION FRAMEWORK TO LARGE DIMENSIONAL TRANSFER LEARNING
         Romain Couillet; GIPSA-lab, University of Grenoble-Alpes
 
  TA1.3: PERFORMANCE ANALYSIS OF A LOW-RANK DETECTOR UNDER TRAINING DATA CONTAMINATION
         Pascal Vallet; Bordeaux INP
         Guillaume Ginolhac; Polytech Annecy-Chambéry
         Frédéric Pascal; CentraleSupélec
         Philippe Forster; Université Paris-Nanterre
 
  TA1.4: PROBABILITY OF RESOLUTION OF PARTIALLY RELAXED DML AN ASYMPTOTIC APPROACH
         David Schenck; Technische Universität Darmstadt
         Xavier Mestre; Centre Tecnològic de Telecomunicacions de Catalunya
         Marius Pesavento; Technische Universität Darmstadt
 
  TA1.5: PHASE TRANSITION IN THE HARD-MARGIN SUPPORT VECTOR MACHINES
         Houssem Sifaou; King Abdullah University of Science and Technology
         Abla Kammoun; King Abdullah University of Science and Technology
         Mohamed-Slim Alouini; King Abdullah University of Science and Technology
 
  TA1.6: HIGH DIMENSIONAL ROBUST CLASSIFICATION: A RANDOM MATRIX ANALYSIS
         Romain Couillet; GIPSA-lab, University of Grenoble-Alpes