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