6 citations · 6 across the 3 of their papers we have counts for
4 papers
On Accelerating Distributed Convex Optimizations
Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra
This paper studies a distributed multi-agent convex optimization problem. The system comprises multiple agents in this problem, each with a set of local data points and an associat…
Generalized AdaGrad (G-AdaGrad) and Adam: A State-Space Perspective
Kushal Chakrabarti, Nikhil Chopra
Accelerated gradient-based methods are being extensively used for solving non-convex machine learning problems, especially when the data points are abundant or the available data i…
Accelerating Distributed SGD for Linear Regression using Iterative Pre-Conditioning
Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra
This paper considers the multi-agent distributed linear least-squares problem. The system comprises multiple agents, each agent with a locally observed set of data points, and a co…
Iterative Pre-Conditioning to Expedite the Gradient-Descent Method
Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra
This paper considers the problem of multi-agent distributed optimization. In this problem, there are multiple agents in the system, and each agent only knows its local cost functio…