7 citations · 9 across the 8 of their papers we have counts for
13 papers
Noisy-Syndrome Decoding of Hypergraph Product Codes
Venkata Gandikota, Elena Grigorescu, Vatsal Jha +1
Hypergraph product codes are a prototypical family of quantum codes with state-of-the-art decodability properties. In this work we consider the "noisy" syndrome decoding problem an…
Optimization using Parallel Gradient Evaluations on Multiple Parameters
Yash Chandak, Shiv Shankar, Venkata Gandikota +2
We propose a first-order method for convex optimization, where instead of being restricted to the gradient from a single parameter, gradients from multiple parameters can be used d…
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…
Superset Technique for Approximate Recovery in One-Bit Compressed Sensing
Larkin Flodin, Venkata Gandikota, Arya Mazumdar
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 ana…
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…
Nearly Optimal Sparse Group Testing
Venkata Gandikota, Elena Grigorescu, Sidharth Jaggi +1
Group testing is the process of pooling arbitrary subsets from a set of items so as to identify, with a minimal number of tests, a "small" subset of defective items. In "cl…
NP-Hardness of Reed-Solomon Decoding, and the Prouhet-Tarry-Escott Problem
Venkata Gandikota, Badih Ghazi, Elena Grigorescu
Establishing the complexity of {\em Bounded Distance Decoding} for Reed-Solomon codes is a fundamental open problem in coding theory, explicitly asked by Guruswami and Vardy (IEEE…
Local Testing for Membership in Lattices
Karthekeyan Chandrasekaran, Mahdi Cheraghchi, Venkata Gandikota +1
Motivated by the structural analogies between point lattices and linear error-correcting codes, and by the mature theory on locally testable codes, we initiate a systematic study o…
Deciding Orthogonality in Construction-A Lattices
Karthekeyan Chandrasekaran, Venkata Gandikota, Elena Grigorescu
Lattices are discrete mathematical objects with widespread applications to integer programs as well as modern cryptography. A fundamental problem in both domains is the Closest Vec…