6 papers
Support Recovery of Sparse Signals from a Mixture of Linear Measurements
Venkata Gandikota, Arya Mazumdar, Soumyabrata Pal
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 mo…
Recovery of sparse linear classifiers from mixture of responses
Venkata Gandikota, Arya Mazumdar, Soumyabrata Pal
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 queryi…
Reliable Distributed Clustering with Redundant Data Assignment
Venkata Gandikota, Arya Mazumdar, Ankit Singh Rawat
In this paper, we present distributed generalized clustering algorithms that can handle large scale data across multiple machines in spite of straggling or unreliable machines. We…
vqSGD: Vector Quantized Stochastic Gradient Descent
Venkata Gandikota, Daniel Kane, Raj Kumar Maity +1
In this work, we present a family of vector quantization schemes \emph{vqSGD} (Vector-Quantized Stochastic Gradient Descent) that provide an asymptotic reduction in the communicati…
Relaxed Locally Correctable Codes in Computationally Bounded Channels
Jeremiah Blocki, Venkata Gandikota, Elena Grigorescu +1
Error-correcting codes that admit local decoding and correcting algorithms have been the focus of much recent research due to their numerous theoretical and practical applications.…
Lattice-based Locality Sensitive Hashing is Optimal
Karthekeyan Chandrasekaran, Daniel Dadush, Venkata Gandikota +1
Locality sensitive hashing (LSH) was introduced by Indyk and Motwani (STOC `98) to give the first sublinear time algorithm for the c-approximate nearest neighbor (ANN) problem usin…