Nirupam Gupta
Nirupam Gupta
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Title
Cited by
Cited by
Year
Privacy in distributed average consensus
N Gupta, J Katz, N Chopra
IFAC-PapersOnLine 50 (1), 9515-9520, 2017
262017
On content modification attacks in bilateral teleoperation systems
Y Dong, N Gupta, N Chopra
2016 American Control Conference (ACC), 316-321, 2016
112016
Byzantine fault tolerant distributed linear regression
N Gupta, NH Vaidya
arXiv preprint arXiv:1903.08752, 2019
102019
Statistical Privacy in Distributed Average Consensus on Bounded Real Inputs
N Gupta, J Katz, N Chopra
2019 American Control Conference (ACC), 1836-1841, 2019
92019
Confidentiality in distributed average information consensus
N Gupta, N Chopra
2016 IEEE 55th Conference on Decision and Control (CDC), 6709-6714, 2016
92016
Byzantine fault-tolerant parallelized stochastic gradient descent for linear regression
N Gupta, NH Vaidya
2019 57th Annual Allerton Conference on Communication, Control, and …, 2019
72019
Content modification attacks on consensus seeking multi-agent system with double-integrator dynamics
Y Dong, N Gupta, N Chopra
Chaos: An Interdisciplinary Journal of Nonlinear Science 26 (11), 116305, 2016
72016
Fault-tolerance in distributed optimization: The case of redundancy
N Gupta, NH Vaidya
Proceedings of the 39th Symposium on Principles of Distributed Computing …, 2020
52020
Resilience in collaborative optimization: redundant and independent cost functions
N Gupta, NH Vaidya
arXiv preprint arXiv:2003.09675, 2020
52020
Iterative Pre-Conditioning to Expedite the Gradient-Descent Method
K Chakrabarti, N Gupta, N Chopra
2020 American Control Conference (ACC), 3977-3982, 2020
52020
Information-theoretic privacy in distributed average consensus
N Gupta, J Katz, N Chopra
arXiv preprint arXiv:1809.01794, 2018
52018
Byzantine Fault-Tolerant Distributed Machine Learning Using Stochastic Gradient Descent (SGD) and Norm-Based Comparative Gradient Elimination (CGE)
N Gupta, S Liu, NH Vaidya
arXiv preprint arXiv:2008.04699, 2020
22020
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
22020
False data injection attacks in bilateral teleoperation systems
Y Dong, N Gupta, N Chopra
IEEE Transactions on Control Systems Technology 28 (3), 1168-1176, 2019
22019
Privacy in distributed multi-agent collaboration: Consensus and optimization
N Gupta
22018
Robustness of distributive double-integrator consensus to loss of graph connectivity
N Gupta, Y Dong, N Chopra
2017 American Control Conference (ACC), 4516-4521, 2017
22017
Stability analysis of a two-channel feedback networked control system
N Gupta, N Chopra
2016 Indian Control Conference (ICC), 200-207, 2016
22016
Preserving Statistical Privacy in Distributed Optimization
N Gupta, S Gade, N Chopra, NH Vaidya
IEEE Control Systems Letters 5 (3), 779-784, 2021
12021
Differential Privacy and Byzantine Resilience in SGD: Do They Add Up?
R Guerraoui, N Gupta, R Pinot, S Rouault, J Stephan
arXiv preprint arXiv:2102.08166, 2021
2021
Byzantine Fault-Tolerance in Peer-to-Peer Distributed Gradient-Descent
N Gupta, NH Vaidya
arXiv preprint arXiv:2101.12316, 2021
2021
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