29 citations · 71 across the 48 of their papers we have counts for
11 papers · 1 filter
Calibrating AI Models for Wireless Communications via Conformal Prediction
Kfir M. Cohen, Sangwoo Park, Osvaldo Simeone +1
When used in complex engineered systems, such as communication networks, artificial intelligence (AI) models should be not only as accurate as possible, but also well calibrated. A…
Fast-Convergent Federated Learning via Cyclic Aggregation
Youngjoon Lee, Sangwoo Park, Joonhyuk Kang
Federated learning (FL) aims at optimizing a shared global model over multiple edge devices without transmitting (private) data to the central server. While it is theoretically wel…
Security-Preserving Federated Learning via Byzantine-Sensitive Triplet Distance
Youngjoon Lee, Sangwoo Park, Joonhyuk Kang
While being an effective framework of learning a shared model across multiple edge devices, federated learning (FL) is generally vulnerable to Byzantine attacks from adversarial ed…
Calibrating AI Models for Few-Shot Demodulation via Conformal Prediction
Kfir M. Cohen, Sangwoo Park, Osvaldo Simeone +1
AI tools can be useful to address model deficits in the design of communication systems. However, conventional learning-based AI algorithms yield poorly calibrated decisions, unabl…
Few-Shot Calibration of Set Predictors via Meta-Learned Cross-Validation-Based Conformal Prediction
Sangwoo Park, Kfir M. Cohen, Osvaldo Simeone
Conventional frequentist learning is known to yield poorly calibrated models that fail to reliably quantify the uncertainty of their decisions. Bayesian learning can improve calibr…
Learning with Limited Samples -- Meta-Learning and Applications to Communication Systems
Lisha Chen, Sharu Theresa Jose, Ivana Nikoloska +3
Deep learning has achieved remarkable success in many machine learning tasks such as image classification, speech recognition, and game playing. However, these breakthroughs are of…
Continual Meta-Reinforcement Learning for UAV-Aided Vehicular Wireless Networks
Riccardo Marini, Sangwoo Park, Osvaldo Simeone +1
Unmanned aerial base stations (UABSs) can be deployed in vehicular wireless networks to support applications such as extended sensing via vehicle-to-everything (V2X) services. A ke…
Robust Bayesian Learning for Reliable Wireless AI: Framework and Applications
Matteo Zecchin, Sangwoo Park, Osvaldo Simeone +2
This work takes a critical look at the application of conventional machine learning methods to wireless communication problems through the lens of reliability and robustness. Deep…
Predicting Multi-Antenna Frequency-Selective Channels via Meta-Learned Linear Filters based on Long-Short Term Channel Decomposition
Sangwoo Park, Osvaldo Simeone
An efficient data-driven prediction strategy for multi-antenna frequency-selective channels must operate based on a small number of pilot symbols. This paper proposes novel channel…
Online Meta-Learning For Hybrid Model-Based Deep Receivers
Tomer Raviv, Sangwoo Park, Osvaldo Simeone +2
Recent years have witnessed growing interest in the application of deep neural networks (DNNs) for receiver design, which can potentially be applied in complex environments without…
Robust PAC: Training Ensemble Models Under Misspecification and Outliers
Matteo Zecchin, Sangwoo Park, Osvaldo Simeone +2
Standard Bayesian learning is known to have suboptimal generalization capabilities under misspecification and in the presence of outliers. PAC-Bayes theory demonstrates that the fr…