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20212024
most citedRobust Black-box Testing of Deep Neural Networks using Co-Domain Coverage

1 citations · 1 across the 5 of their papers we have counts for

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5 papers

cs.LG2024

Federated Learning with Uncertainty and Personalization via Efficient Second-order Optimization

Shivam Pal, Aishwarya Gupta, Saqib Sarwar +1

Federated Learning (FL) has emerged as a promising method to collaboratively learn from decentralized and heterogeneous data available at different clients without the requirement…

cs.LG2024★ 1 cited

Robust Black-box Testing of Deep Neural Networks using Co-Domain Coverage

Aishwarya Gupta, Indranil Saha, Piyush Rai

Rigorous testing of machine learning models is necessary for trustworthy deployments. We present a novel black-box approach for generating test-suites for robust testing of deep ne…

cs.LG2023

Insuring Smiles: Predicting routine dental coverage using Spark ML

Aishwarya Gupta, Rahul S. Bhogale, Priyanka Thota +2

Finding suitable health insurance coverage can be challenging for individuals and small enterprises in the USA. The Health Insurance Exchange Public Use Files (Exchange PUFs) datas…

cs.LG2022

Federated Learning with Uncertainty via Distilled Predictive Distributions

Shrey Bhatt, Aishwarya Gupta, Piyush Rai

Most existing federated learning methods are unable to estimate model/predictive uncertainty since the client models are trained using the standard loss function minimization appro…

cs.CL2021

Humor@IITK at SemEval-2021 Task 7: Large Language Models for Quantifying Humor and Offensiveness

Aishwarya Gupta, Avik Pal, Bholeshwar Khurana +2

Humor and Offense are highly subjective due to multiple word senses, cultural knowledge, and pragmatic competence. Hence, accurately detecting humorous and offensive texts has seve…