7 accepted papers
Group testing is the problem of identifying a small subset of defectives from a large set using as few binary tests as possible. In most current literature on group testing the binary test outcome is $1$ if the pool contains at least one defective, and $0$ otherwise. In this work we initiate the stu…
Point lattices play an important role in various fields of computer science, including communication, cryptography, optimization, and machine learning. In this work, we introduce the concept of Locally Correctable Lattices (LCLs)—a class of lattices with an efficient reconstruction algorithm that ca…
1-bit compressed sensing (1bCS) is a quantized signal acquisition technique to compress high-dimensional sparse signals. The goal is to design sensing matrices A ∈ ℝ<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m×n</sup> with the fewest possible rows…
Recovery of support of a sparse vector from simple measurements is a widely studied problem, considered under the frameworks of compressed sensing, 1-bit compressed sensing, and more general single index models. We consider generalizations of this problem: mixtures of linear regressions, and mixture…
In this work, we present a family of vector quantization schemes vqSGD (Vector-Quantized Stochastic Gradient Descent) that provide an asymptotic reduction in the communication cost with convergence guarantees in first-order distributed optimization. In the process we derive the following fundamental…
In the problem of learning a mixture of linear classifiers, the aim is to learn a collection of hyperplanes from a sequence of binary responses. Each response is a result of querying with a vector and indicates the side of a randomly chosen hyperplane from the collection the query vector belong to.…
One-bit compressed sensing (1bCS) is a method of signal acquisition under extreme measurement quantization that gives important insights on the limits of signal compression and analog-to-digital conversion. The setting is also equivalent to the problem of learning a sparse hyperplane-classifier. In…