6 citations · 7 across the 5 of their papers we have counts for
7 papers
Co-Design of Lipschitz Nonlinear Systems
Prasad Vilas Chanekar, Nikhil Chopra
Empirical experiences have shown that simultaneous (rather than conventional sequential) plant and controller design procedure leads to an improvement in performance and saving of…
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…
Adaptive Tracking Control of Soft Robots using Integrated Sensing Skin and Recurrent Neural Networks
Lasitha Weerakoon, Zepeng Ye, Rahul Subramonian Bama +3
In this paper, we study integrated estimation and control of soft robots. A significant challenge in deploying closed loop controllers is reliable proprioception via integrated sen…
Preserving Statistical Privacy in Distributed Optimization
Nirupam Gupta, Shripad Gade, Nikhil Chopra +1
We present a distributed optimization protocol that preserves statistical privacy of agents' local cost functions against a passive adversary that corrupts some agents in the netwo…