106 citations · 112 across the 13 of their papers we have counts for
13 papers
Unequal Opportunities: Examining the Bias in Geographical Recommendations by Large Language Models
Shiran Dudy, Thulasi Tholeti, Resmi Ramachandranpillai +3
Recent advancements in Large Language Models (LLMs) have made them a popular information-seeking tool among end users. However, the statistical training methods for LLMs have raise…
Online waveform selection for cognitive radar
Thulasi Tholeti, Avinash Rangarajan, Sheetal Kalyani
Designing a cognitive radar system capable of adapting its parameters is challenging, particularly when tasked with tracking a ballistic missile throughout its entire flight. In th…
Randomness Helps Rigor: A Probabilistic Learning Rate Scheduler Bridging Theory and Deep Learning Practice
Dahlia Devapriya, Thulasi Tholeti, Janani Suresh +1
Learning rate schedulers have shown great success in speeding up the convergence of learning algorithms in practice. However, their convergence to a minimum has not been proven the…
Introducing the Huber mechanism for differentially private low-rank matrix completion
R Adithya Gowtham, Gokularam M, Thulasi Tholeti +1
Performing low-rank matrix completion with sensitive user data calls for privacy-preserving approaches. In this work, we propose a novel noise addition mechanism for preserving dif…
The robust way to stack and bag: the local Lipschitz way
Thulasi Tholeti, Sheetal Kalyani
Recent research has established that the local Lipschitz constant of a neural network directly influences its adversarial robustness. We exploit this relationship to construct an e…
How to boost autoencoders?
Sai Krishna, Thulasi Tholeti, Sheetal Kalyani
Autoencoders are a category of neural networks with applications in numerous domains and hence, improvement of their performance is gaining substantial interest from the machine le…
Binarized ResNet: Enabling Robust Automatic Modulation Classification at the resource-constrained Edge
Deepsayan Sadhukhan, Nitin Priyadarshini Shankar, Nancy Nayak +2
Recently, deep neural networks (DNNs) have been used extensively for automatic modulation classification (AMC), and the results have been quite promising. However, DNNs have high m…
On the Differentially Private Nature of Perturbed Gradient Descent
Thulasi Tholeti, Sheetal Kalyani
We consider the problem of empirical risk minimization given a database, using the gradient descent algorithm. We note that the function to be optimized may be non-convex, consisti…
Tune smarter not harder: A principled approach to tuning learning rates for shallow nets
Thulasi Tholeti, Sheetal Kalyani
Effective hyper-parameter tuning is essential to guarantee the performance that neural networks have come to be known for. In this work, a principled approach to choosing the learn…
Green DetNet: Computation and Memory efficient DetNet using Smart Compression and Training
Nancy Nayak, Thulasi Tholeti, Muralikrishnan Srinivasan +1
This paper introduces an incremental training framework for compressing popular Deep Neural Network (DNN) based unfolded multiple-input-multiple-output (MIMO) detection algorithms…
Concavifiability and convergence: necessary and sufficient conditions for gradient descent analysis
Thulasi Tholeti, Sheetal Kalyani
Convergence of the gradient descent algorithm has been attracting renewed interest due to its utility in deep learning applications. Even as multiple variants of gradient descent w…
A Centralized Multi-stage Non-parametric Learning Algorithm for Opportunistic Spectrum Access
Thulasi Tholeti, Vishnu Raj, Sheetal Kalyani
Owing to the ever-increasing demand in wireless spectrum, Cognitive Radio (CR) was introduced as a technique to attain high spectral efficiency. As the number of secondary users (S…
Spectrum Access In Cognitive Radio Using A Two Stage Reinforcement Learning Approach
Vishnu Raj, Irene Dias, Thulasi Tholeti +1
With the advent of the 5th generation of wireless standards and an increasing demand for higher throughput, methods to improve the spectral efficiency of wireless systems have beco…