MO3.R9.2

Low-rank Matrix Sensing With Dithered One-Bit Quantization

Farhang Yeganegi, Arian Eamaz, Mojtaba Soltanalian, University of Illinois Chicago, United States
Session:
Statistical Estimation and Detection
Track:
11: Information Theory and Statistics
Location:
Lamda
Presentation Time:
Mon, 8 Jul, 14:55 - 15:15
Session Chair:
Shirin Jalali, Rutgers
Presentation
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Discussion
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Session MO3.R9
MO3.R9.1: Missing Mass under Random Duplications
Prafulla Chandra, Andrew Thangaraj, IIT Madras, India
MO3.R9.2: Low-rank Matrix Sensing With Dithered One-Bit Quantization
Farhang Yeganegi, Arian Eamaz, Mojtaba Soltanalian, University of Illinois Chicago, United States
MO3.R9.3: Sharper rates of convergence for the tensor graphical Lasso estimator
Shuheng Zhou, University of California, United States; Kristjan Greenewald, IBM Research, United States
MO3.R9.4: Detection of Signals in Colored Noise: Leading Eigenvalue Test for Non-central F-matrices
Prathapasinghe Dharmawansa, University of Melbourne, Australia; Saman Atapattu, RMIT University, Australia; Jamie Evans, University of Melbourne, Australia; Kandeepan Sithamparanathan, RMIT University, Australia
Resources
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