Technical Program

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MLSP-P9: Graphical, Kernel and Tensor Methods

Session Type: Poster
Time: Thursday, 7 May, 09:00 - 11:00
Location: On-Demand
Virtual Session: View on Virtual Platform
Session Chair: Kejun Huang, University of Florida
 
 MLSP-P9.1: KERNEL RIDGE REGRESSION WITH AUTOCORRELATION PRIOR: OPTIMAL MODEL AND CROSS-VALIDATION
         Akira Tanaka; Hokkaido University
         Hideyuki Imai; Hokkaido University
 
 MLSP-P9.2: GENERALIZED KERNEL-BASED DYNAMIC MODE DECOMPOSITION
         Patrick Héas; Inria & Irmar, Univ. Rennes
         Cédric Herzet; Inria & Irmar, Univ. Rennes
         Benoit Combès; Inria & Irisa, Univ. Rennes
 
 MLSP-P9.3: AN ONLINE KERNEL SCALAR QUANTIZATION SCHEME FOR SIGNAL CLASSIFICATION
         Jing Guo; Purdue University
         Raghu Raj; U.S. Naval Research Laboratory
         David Love; Purdue University
 
 MLSP-P9.4: SELF-DRIVEN GRAPH VOLTERRA MODELS FOR HIGHER-ORDER LINK PREDICTION
         Mario Coutino; Delft University of Technology
         Georgios V. Karanikolas; University of Minnesota
         Geert Leus; Delft University of Technology
         Georgios B. Giannakis; University of Minnesota
 
 MLSP-P9.5: GRAPH CONSTRUCTION FROM DATA BY NON-NEGATIVE KERNEL REGRESSION
         Sarath Shekkizhar; University of Southern California
         Antonio Ortega; University of Southern California
 
 MLSP-P9.6: STRUCTURED CITATION TREND PREDICTION USING GRAPH NEURAL NETWORKS
         Daniel Cummings; Intel
         Marcel Nassar; Intel
 
 MLSP-P9.7: REVISITING FAST SPECTRAL CLUSTERING WITH ANCHOR GRAPH
         Cheng-Long Wang; Northwestern Polytechnical University
         Feiping Nie; Northwestern Polytechnical University
         Rong Wang; Northwestern Polytechnical University
         Xuelong Li; Northwestern Polytechnical University
 
 MLSP-P9.8: A GRAPH NETWORK MODEL FOR DISTRIBUTED LEARNING WITH LIMITED BANDWIDTH LINKS AND PRIVACY CONSTRAINTS
         Juan Parras; Universidad Politécnica de Madrid
         Santiago Zazo; Universidad Politécnica de Madrid
 
 MLSP-P9.9: GRAPH REGULARIZED TENSOR TRAIN DECOMPOSITION
         Seyyid Emre Sofuoglu; Michigan State University
         Selin Aviyente; Michigan State University
 
 MLSP-P9.10: WEIGHTED KRYLOV-LEVENBERG-MARQUARDT METHOD FOR CANONICAL POLYADIC TENSOR DECOMPOSITION
         Petr Tichavsky; Academy of Sciences of the Czech Republic
         Anh-Huy Phan; Skolkovo Institute of Science and Technology (Skoltech)
         Andrzej Cichocki; Skolkovo Institute of Science and Technology (Skoltech)
 
 MLSP-P9.11: LOW-COMPLEXITY LEVENBERG-MARQUARDT ALGORITHM FOR TENSOR CANONICAL POLYADIC DECOMPOSITION
         Kejun Huang; University of Florida
         Xiao Fu; Oregon State University
 
 MLSP-P9.12: A MOMENT-BASED APPROACH FOR GUARANTEED TENSOR DECOMPOSITION
         Arthur Marmin; Université Paris-Saclay, CentraleSupélec, Inria
         Marc Castella; CNRS, Télécom SudParis, Institut Polytechnique de Paris
         Jean-Christophe Pesquet; Université Paris-Saclay, CentraleSupélec, Inria