4 citations · 6 across the 4 of their papers we have counts for
4 papers
Learning sparse generalized linear models with binary outcomes via iterative hard thresholding
Namiko Matsumoto, Arya Mazumdar
In statistics, generalized linear models (GLMs) are widely used for modeling data and can expressively capture potential nonlinear dependence of the model's outcomes on its covaria…
Robust 1-bit Compressed Sensing with Iterative Hard Thresholding
Namiko Matsumoto, Arya Mazumdar
In 1-bit compressed sensing, the aim is to estimate a -sparse unit vector within an error (in ) from minimal number of linear measurements that are qu…
Improved Support Recovery in Universal One-bit Compressed Sensing
Namiko Matsumoto, Arya Mazumdar, Soumyabrata Pal
One-bit compressed sensing (1bCS) is an extremely quantized signal acquisition method that has been proposed and studied rigorously in the past decade. In 1bCS, linear samples of a…
Binary Iterative Hard Thresholding Converges with Optimal Number of Measurements for 1-Bit Compressed Sensing
Namiko Matsumoto, Arya Mazumdar
Compressed sensing has been a very successful high-dimensional signal acquisition and recovery technique that relies on linear operations. However, the actual measurements of signa…