41 citations · 119 across the 27 of their papers we have counts for
29 papers
Fairness Uncertainty Quantification: How certain are you that the model is fair?
Abhishek Roy, Prasant Mohapatra
Fairness-aware machine learning has garnered significant attention in recent years because of extensive use of machine learning in sensitive applications like judiciary systems. Va…
Decentralized Multi-agent Filtering
Dom Huh, Prasant Mohapatra
This paper addresses the considerations that comes along with adopting decentralized communication for multi-agent localization applications in discrete state spaces. In this frame…
Federated Learning Hyper-Parameter Tuning from a System Perspective
Huanle Zhang, Lei Fu, Mi Zhang +4
Federated learning (FL) is a distributed model training paradigm that preserves clients' data privacy. It has gained tremendous attention from both academia and industry. FL hyper-…
On the Robustness of Deep Clustering Models: Adversarial Attacks and Defenses
Anshuman Chhabra, Ashwin Sekhari, Prasant Mohapatra
Clustering models constitute a class of unsupervised machine learning methods which are used in a number of application pipelines, and play a vital role in modern data science. Wit…
Robust Fair Clustering: A Novel Fairness Attack and Defense Framework
Anshuman Chhabra, Peizhao Li, Prasant Mohapatra +1
Clustering algorithms are widely used in many societal resource allocation applications, such as loan approvals and candidate recruitment, among others, and hence, biased or unfair…
Iota: A Framework for Analyzing System-Level Security of IoTs
Zheng Fang, Hao Fu, Tianbo Gu +4
Most IoT systems involve IoT devices, communication protocols, remote cloud, IoT applications, mobile apps, and the physical environment. However, existing IoT security analyses on…