Segui
Kushal Chakrabarti
Kushal Chakrabarti
Consultant, Tata Consultancy Services Research
Email verificata su terpmail.umd.edu - Home page
Titolo
Citata da
Citata da
Anno
Generalized AdaGrad (G-AdaGrad) and Adam: A state-space perspective
K Chakrabarti, N Chopra
2021 60th IEEE Conference on Decision and Control (CDC), 1496-1501, 2021
122021
Iterative pre-conditioning to expedite the gradient-descent method
K Chakrabarti, N Gupta, N Chopra
2020 American Control Conference (ACC), 3977-3982, 2020
102020
Robustness of Iteratively Pre-Conditioned Gradient-Descent Method: The Case of Distributed Linear Regression Problem
K Chakrabarti, N Gupta, N Chopra
2021 American Control Conference (ACC), 2248-2253, 2021
62021
Iterative pre-conditioning for expediting the distributed gradient-descent method: The case of linear least-squares problem
K Chakrabarti, N Gupta, N Chopra
Automatica 137, 110095, 2022
52022
On Accelerating Distributed Convex Optimizations
K Chakrabarti, N Gupta, N Chopra
arXiv preprint arXiv:2108.08670, 2021
52021
Iterative pre-conditioning for expediting the gradient-descent method: The distributed linear least-squares problem
K Chakrabarti, N Gupta, N Chopra
arXiv preprint arXiv:2008.02856, 2020
52020
A control theoretic framework for adaptive gradient optimizers in machine learning
K Chakrabarti, N Chopra
arXiv preprint arXiv:2206.02034, 2022
42022
Accelerating distributed SGD for linear regression using iterative pre-conditioning
K Chakrabarti, N Gupta, N Chopra
Learning for Dynamics and Control, 447-458, 2021
32021
Fast distributed beamforming without receiver feedback
K Chakrabarti, AS Bedi, FT Dagefu, JN Twigg, N Chopra
2022 56th Asilomar Conference on Signals, Systems, and Computers, 1408-1412, 2022
22022
Iteratively preconditioned gradient-descent approach for moving horizon estimation problems
T Liu, K Chakrabarti, N Chopra
2023 62nd IEEE Conference on Decision and Control (CDC), 8457-8462, 2023
12023
IPG Observer: A Newton-Type Observer Robust to Measurement Noise
K Chakrabarti, N Chopra
2023 American Control Conference (ACC), 3069-3074, 2023
12023
A state-space perspective on the expedited gradient methods: Nadam, RAdam, and rescaled gradient flow
K Chakrabarti, N Chopra
2022 Eighth Indian Control Conference (ICC), 31-36, 2022
12022
Analysis and Synthesis of Adaptive Gradient Algorithms in Machine Learning: The Case of AdaBound and MAdamSSM
K Chakrabarti, N Chopra
2022 IEEE 61st Conference on Decision and Control (CDC), 795-800, 2022
12022
On Preconditioning of Decentralized Gradient-Descent When Solving a System of Linear Equations
K Chakrabarti, N Gupta, N Chopra
IEEE Transactions on Control of Network Systems 9 (2), 811-822, 2022
12022
Control Theory-Inspired Acceleration of the Gradient-Descent Method: Centralized and Distributed
K Chakrabarti
University of Maryland, College Park, 2022
12022
A Kalman filter approach for biomolecular systems with noise covariance updating
A Dey, K Chakrabarti, KK Gola, S Sen
2019 Sixth Indian Control Conference (ICC), 262-267, 2019
12019
On Distributed Solution of Ill-Conditioned System of Linear Equations under Communication Delays
K Chakrabarti, N Gupta, N Chopra
2019 Sixth Indian Control Conference (ICC), 413-418, 2019
12019
A control theoretic framework for adaptive gradient optimizers
K Chakrabarti, N Chopra
Automatica 160, 111466, 2024
2024
Accelerating the Iteratively Preconditioned Gradient-Descent Algorithm using Momentum
T Liu, K Chakrabarti, N Chopra
2023 Ninth Indian Control Conference (ICC), 68-73, 2023
2023
Linear Convergence of Pre-Conditioned PI Consensus Algorithm under Restricted Strong Convexity
K Chakrabarti, M Baranwal
arXiv preprint arXiv:2310.00419, 2023
2023
Il sistema al momento non può eseguire l'operazione. Riprova più tardi.
Articoli 1–20