Search Results for author: Venkata Gandikota

Found 5 papers, 0 papers with code

Optimization using Parallel Gradient Evaluations on Multiple Parameters

no code implementations6 Feb 2023 Yash Chandak, Shiv Shankar, Venkata Gandikota, Philip S. Thomas, Arya Mazumdar

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 during each step of gradient descent.

Support Recovery of Sparse Signals from a Mixture of Linear Measurements

no code implementations NeurIPS 2021 Venkata Gandikota, Arya Mazumdar, Soumyabrata Pal

In this work, we study the number of measurements sufficient for recovering the supports of all the component vectors in a mixture in both these models.

Recovery of sparse linear classifiers from mixture of responses

no code implementations NeurIPS 2020 Venkata Gandikota, Arya Mazumdar, Soumyabrata Pal

We look at a hitherto unstudied problem of query complexity upper bound of recovering all the hyperplanes, especially for the case when the hyperplanes are sparse.


Reliable Distributed Clustering with Redundant Data Assignment

no code implementations20 Feb 2020 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.

Clustering Dimensionality Reduction

vqSGD: Vector Quantized Stochastic Gradient Descent

no code implementations18 Nov 2019 Venkata Gandikota, Daniel Kane, Raj Kumar Maity, Arya Mazumdar

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 communication cost with convergence guarantees in first-order distributed optimization.

Distributed Optimization Quantization

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