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Thulasi Tholeti
Thulasi Tholeti
Postdoctoral Research Fellow, EAI, Northeastern University
Verified email at northeastern.edu
Title
Cited by
Cited by
Year
Spectrum access in cognitive radio using a two-stage reinforcement learning approach
V Raj, I Dias, T Tholeti, S Kalyani
IEEE Journal of Selected Topics in Signal Processing 12 (1), 20-34, 2018
1002018
The robust way to stack and bag: the local Lipschitz way
T Tholeti, S Kalyani
arXiv preprint arXiv:2206.00513, 2022
22022
Green detnet: Computation and memory efficient detnet using smart compression and training
N Nayak, T Tholeti, M Srinivasan, S Kalyani
arXiv preprint arXiv:2003.09446, 2020
22020
A Centralized Multi-stage Non-parametric Learning Algorithm for Opportunistic Spectrum Access
T Tholeti, V Raj, S Kalyani
arXiv preprint arXiv:1804.11135, 2018
2*2018
Binarized ResNet: Enabling Robust Automatic Modulation Classification at the Resource-Constrained Edge
NP Shankar, D Sadhukhan, N Nayak, T Tholeti, S Kalyani
IEEE Transactions on Cognitive Communications and Networking, 2024
2024
Introducing the Huber mechanism for differentially private low-rank matrix completion
RA Gowtham, T Tholeti, S Kalyani
arXiv preprint arXiv:2206.07910, 2022
2022
Introducing the Huber mechanism for differentially private low-rank matrix completion
R Adithya Gowtham, T Tholeti, S Kalyani
arXiv e-prints, arXiv: 2206.07910, 2022
2022
How to boost autoencoders?
S Krishna, T Tholeti, S Kalyani
arXiv preprint arXiv:2110.15307, 2021
2021
Binarized ResNet: Enabling Robust Automatic Modulation Classification at the resource-constrained Edge
D Sadhukhan, NP Shankar, N Nayak, T Tholeti, S Kalyani
arXiv preprint arXiv:2110.14357, 2021
2021
On the Differentially Private Nature of Perturbed Gradient Descent
T Tholeti, S Kalyani
arXiv preprint arXiv:2101.06847, 2021
2021
Tune smarter not harder: A principled approach to tuning learning rates for shallow nets
T Tholeti, S Kalyani
IEEE Transactions on Signal Processing 68, 5063-5078, 2020
2020
Concavifiability and convergence: necessary and sufficient conditions for gradient descent analysis
T Tholeti, S Kalyani
arXiv preprint arXiv:1905.11620, 2019
2019
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