7 citations · 18 across the 29 of their papers we have counts for
5 papers · 1 filter
Diffusion-based Decentralized Federated Multi-Task Representation Learning
Donghwa Kang, Shana Moothedath
Representation learning is a widely adopted framework for learning in data-scarce environments to obtain a feature extractor or representation from various different yet related ta…
Beyond Centralization: Provable Communication Efficient Decentralized Multi-Task Learning
Donghwa Kang, Shana Moothedath
Representation learning is a widely adopted framework for learning in data-scarce environments, aiming to extract common features from related tasks. While centralized approaches h…
Energy-Aware 6G Network Design: A Survey
Rashmi Kamran, Mahesh Ganesh Bhat, Pranav Jha +3
6th Generation (6G) mobile networks are envisioned to support several new capabilities and data-centric applications for unprecedented number of users, potentially raising signific…
Neural Contextual Bandits Under Delayed Feedback Constraints
Mohammadali Moghimi, Sharu Theresa Jose, Shana Moothedath
This paper presents a new algorithm for neural contextual bandits (CBs) that addresses the challenge of delayed reward feedback, where the reward for a chosen action is revealed af…
Byzantine Resilient Federated Multi-Task Representation Learning
Tuan Le, Shana Moothedath
In this paper, we propose BR-MTRL, a Byzantine-resilient multi-task representation learning framework that handles faulty or malicious agents. Our approach leverages representation…