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Found 5 papers, 0 papers with code
Optimization using Parallel Gradient Evaluations on Multiple Parameters
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
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
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
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.
vqSGD: Vector Quantized Stochastic Gradient Descent
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.
