654 citations · 1.9k across the 149 of their papers we have counts for
10 papers · 1 filter
Over-the-Air Federated Learning in Heterogeneous Mobile Wireless Networks
Ming Xiang, Nicolò Michelusi, Yonina C. Eldar +1
Over-the-air computation has emerged as a scalable and efficient solution for deploying federated learning algorithms in wireless networks by exploiting waveform superposition for…
SGD-Based Knowledge Distillation with Bayesian Teachers: Theory and Guidelines
Itai Morad, Nir Shlezinger, Yonina C. Eldar
Knowledge Distillation (KD) is a central paradigm for transferring knowledge from a large teacher network to a typically smaller student model, often by leveraging soft probabilist…
Deep Unfolding: Recent Developments, Theory, and Design Guidelines
Nir Shlezinger, Santiago Segarra, Yi Zhang +4
Optimization methods play a central role in signal processing, serving as the mathematical foundation for inference, estimation, and control. While classical iterative optimization…
AI-Aided Kalman Filters
Nir Shlezinger, Guy Revach, Anubhab Ghosh +7
The Kalman filter (KF) and its variants are among the most celebrated algorithms in signal processing. These methods are used for state estimation of dynamic systems by relying on…
Unrolled denoising networks provably learn optimal Bayesian inference
Aayush Karan, Kulin Shah, Sitan Chen +1
Much of Bayesian inference centers around the design of estimators for inverse problems which are optimal assuming the data comes from a known prior. But what do these optimality g…
Communication Efficient ConFederated Learning: An Event-Triggered SAGA Approach
Bin Wang, Jun Fang, Hongbin Li +1
Federated learning (FL) is a machine learning paradigm that targets model training without gathering the local data dispersed over various data sources. Standard FL, which employs…