Iterative Pre-Conditioning to Expedite the Gradient-Descent Method. - Kushal Chakrabarti; Nirupam Gupta; Nikhil Chopra (ACC, 2020)

Iterative Pre-Conditioning for Expediting the Gradient-Descent Method: The Distributed Linear Least-Squares Problem. - Kushal Chakrabarti; Nirupam Gupta; Nikhil Chopra (CoRR, 2020)

Accelerating Distributed SGD for Linear Linear Regression using Iterative Pre-Conditioning. - Kushal Chakrabarti; Nirupam Gupta; Nikhil Chopra (CoRR, 2020)

Generalized AdaGrad (G-AdaGrad) and Adam: A State-Space Perspective. - Kushal Chakrabarti; Nikhil Chopra (CDC, 2021)

Accelerating Distributed SGD for Linear Regression using Iterative Pre-Conditioning. - Kushal Chakrabarti; Nirupam Gupta; Nikhil Chopra (L4DC, 2021)

On Accelerating Distributed Convex Optimizations. - Kushal Chakrabarti; Nirupam Gupta; Nikhil Chopra (CoRR, 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)

Fast Distributed Beamforming without Receiver Feedback. - Kushal Chakrabarti; Amrit S. Bedi; Fikadu T. Dagefu; Jeffrey N. Twigg; Nikhil Chopra (IEEECONF, 2022)

Analysis and Synthesis of Adaptive Gradient Algorithms in Machine Learning: The Case of AdaBound and MAdamSSM. - Kushal Chakrabarti; Nikhil Chopra (CDC, 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)

A Control Theoretic Framework for Adaptive Gradient Optimizers in Machine Learning. - Kushal Chakrabarti; Nikhil Chopra (CoRR, 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)

Control Theory-Inspired Acceleration of the Gradient-Descent Method: Centralized and Distributed. - Kushal Chakrabarti (2022)

IPG Observer: A Newton-Type Observer Robust to Measurement Noise. - Kushal Chakrabarti; Nikhil Chopra (ACC, 2023)

Iteratively Preconditioned Gradient-Descent Approach for Moving Horizon Estimation Problems. - Tianchen Liu; Kushal Chakrabarti; Nikhil Chopra (CDC, 2023)

Quantum Circuit Optimization through Iteratively Pre-Conditioned Gradient Descent. - Dhruv Srinivasan; Kushal Chakrabarti; Nikhil Chopra; Avik Dutt (QCE, 2023)

Linear Convergence of Pre-Conditioned PI Consensus Algorithm under Restricted Strong Convexity. - Kushal Chakrabarti; Mayank Baranwal (CoRR, 2023)

Variant Predictor-Corrector Method for Linear Predictive Control Using Modified Uzawa Algorithm. - Tianchen Liu; Kushal Chakrabarti; Nikhil Chopra (CDC, 2024)

A Methodology Establishing Linear Convergence of Adaptive Gradient Methods under PL Inequality. - Kushal Chakrabarti; Mayank Baranwal (ECAI, 2024)

Distributed Optimization via Energy Conservation Laws in Dilated Coordinates. - Mayank Baranwal; Kushal Chakrabarti (CoRR, 2024)

On Convergence of the Iteratively Preconditioned Gradient-Descent (IPG) Observer. - Kushal Chakrabarti; Nikhil Chopra (IEEE Control. Syst. Lett., 2024)

Novel Iteratively Preconditioned Gradient-Descent Algorithm via Successive Over-Relaxation Formulation. - Tianchen Liu; Kushal Chakrabarti; Nikhil Chopra (IEEE Control. Syst. Lett., 2024)

A control theoretic framework for adaptive gradient optimizers. - Kushal Chakrabarti; Nikhil Chopra (Autom., 2024)

On Model Protection in Federated Learning against Eavesdropping Attacks. - Dipankar Maity; Kushal Chakrabarti (CoRR, 2025)