4 papers
On the Role of Normalization in Binary Iterative Hard Thresholding for 1-bit Compressed Sensing
Arya Mazumdar, Prateeti Mukherjee
Binary Iterative Hard Thresholding (BIHT) is a simple, yet effective, greedy method for recovering a sparse vector from one-bit sign measurements. In its original form, BIHT perfor…
Approximate Distributed Coded Computing: Polynomial Codes and Randomized Sketching
Neophytos Charalambides, Arya Mazumdar
Coded computing is a distributed paradigm that uses coding theory to introduce \textit{redundancy} and overcome bottlenecks in large-scale systems. In the same vein, randomized num…
Support Recovery in One-bit Compressed Sensing with Near-Optimal Measurements and Sublinear Time
Xiaxin Li, Arya Mazumdar
One-bit compressed sensing (1bCS) addresses the recovery of sparse signals from highly quantized measurements, retaining only the sign of each linear measurement. In the support re…
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…