Kushal Chakrabarti

According to our database, Kushal Chakrabarti authored at least 20 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

A control theoretic framework for adaptive gradient optimizers.

Kushal Chakrabarti, Nikhil Chopra

Autom., February, 2024

On Convergence of the Iteratively Preconditioned Gradient-Descent (IPG) Observer.

Kushal Chakrabarti, Nikhil Chopra

IEEE Control. Syst. Lett., 2024

Distributed Optimization via Energy Conservation Laws in Dilated Coordinates.

Mayank Baranwal, Kushal Chakrabarti

CoRR, 2024

A Methodology Establishing Linear Convergence of Adaptive Gradient Methods under PL Inequality.

Kushal Chakrabarti, Mayank Baranwal

Proceedings of the ECAI 2024 - 27th European Conference on Artificial Intelligence, 19-24 October 2024, Santiago de Compostela, Spain, 2024

2023

Linear Convergence of Pre-Conditioned PI Consensus Algorithm under Restricted Strong Convexity.

Kushal Chakrabarti, Mayank Baranwal

CoRR, 2023

Quantum Circuit Optimization through Iteratively Pre-Conditioned Gradient Descent.

Dhruv Srinivasan, Kushal Chakrabarti, Nikhil Chopra, Avik Dutt

Proceedings of the IEEE International Conference on Quantum Computing and Engineering, 2023

Iteratively Preconditioned Gradient-Descent Approach for Moving Horizon Estimation Problems.

Tianchen Liu, Kushal Chakrabarti, Nikhil Chopra

Proceedings of the 62nd IEEE Conference on Decision and Control, 2023

IPG Observer: A Newton-Type Observer Robust to Measurement Noise.

Kushal Chakrabarti, Nikhil Chopra

Proceedings of the American Control Conference, 2023

2022

On Preconditioning of Decentralized Gradient-Descent When Solving a System of Linear Equations.

Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra

IEEE Trans. Control. Netw. Syst., 2022

A Control Theoretic Framework for Adaptive Gradient Optimizers in Machine Learning.

Kushal Chakrabarti, Nikhil Chopra

CoRR, 2022

Iterative pre-conditioning for expediting the distributed gradient-descent method: The case of linear least-squares problem.

Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra

Autom., 2022

Analysis and Synthesis of Adaptive Gradient Algorithms in Machine Learning: The Case of AdaBound and MAdamSSM.

Kushal Chakrabarti, Nikhil Chopra

Proceedings of the 61st IEEE Conference on Decision and Control, 2022

Fast Distributed Beamforming without Receiver Feedback.

Kushal Chakrabarti, Amrit S. Bedi, Fikadu T. Dagefu, Jeffrey N. Twigg, Nikhil Chopra

Proceedings of the 56th Asilomar Conference on Signals, Systems, and Computers, ACSSC 2022, Pacific Grove, CA, USA, October 31, 2022

2021

Robustness of Iteratively Pre-Conditioned Gradient-Descent Method: The Case of Distributed Linear Regression Problem.

Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra

IEEE Control. Syst. Lett., 2021

On Accelerating Distributed Convex Optimizations.

Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra

CoRR, 2021

Accelerating Distributed SGD for Linear Regression using Iterative Pre-Conditioning.

Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra

Proceedings of the 3rd Annual Conference on Learning for Dynamics and Control, 2021

Generalized AdaGrad (G-AdaGrad) and Adam: A State-Space Perspective.

Kushal Chakrabarti, Nikhil Chopra

Proceedings of the 2021 60th IEEE Conference on Decision and Control (CDC), 2021

2020

Accelerating Distributed SGD for Linear Linear Regression using Iterative Pre-Conditioning.

Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra

CoRR, 2020

Iterative Pre-Conditioning for Expediting the Gradient-Descent Method: The Distributed Linear Least-Squares Problem.

Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra

CoRR, 2020

Iterative Pre-Conditioning to Expedite the Gradient-Descent Method.

Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra

Proceedings of the 2020 American Control Conference, 2020