TA6b1.6

On the Asymptotic Linear Convergence of Gradient Descent for Non-Symmetric Matrix Completion

Trung Vu, University of Maryland, Baltimore County, United States; Raviv Raich, Oregon State University, United States
Session:
TA6b1: Algorithms for Adaptive Systems Poster
Track:
Adaptive Systems, Machine Learning, Data Analytics
Location:
Kiln
Presentation Time:
Tue, 31 Oct, 10:15 - 11:55 PT (UTC -8)
Session Chair:
Gonzalo Mateos Buckstein, University of Rochester
Presentation
Discussion
Resources
No resources available.
Session TA6b1
TA6b1.1: Online Network Source Localization from Streaming Graph Signals
Chang Ye, Gonzalo Mateos, University of Rochester, United States
TA6b1.2: Tensor Least Mean Fourth Algorithm
Muhammad Moinuddin, King Abdulaziz University, Saudi Arabia; Azzedine Zerguine, King Fahd University of Petroleum & Minerals, Saudi Arabia
TA6b1.3: Low-Complexity and Model-Free Parameter Tracking Algorithms
Daniel C. Vidal, Vítor H. Nascimento, University of São Paulo, Brazil
TA6b1.4: Advanced Results Verifying that Adaptive Fault Tolerance is Inherently Contained in Bio-Inspired Adaptive Optimization
William Jenkins, Pennsylvania State University, United States; Chandra Radhakrishnan, University of Illinois, United States
TA6b1.5: Teacher-Student Knowledge Distillation for Radar Perception on Embedded Accelerators
Steven Shaw, Kanishka Tyagi, Shan Zhang, Aptiv Technical Center, United States
TA6b1.6: On the Asymptotic Linear Convergence of Gradient Descent for Non-Symmetric Matrix Completion
Trung Vu, University of Maryland, Baltimore County, United States; Raviv Raich, Oregon State University, United States
TA6b1.7: Rayleigh-Ritz based updates of the Multilinear Singular Value Decomposition
Vasileios Kalantzis, IBM Research, United States; Panagiotis Traganitis, Michigan State University, United States
Contacts