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Found 15 papers, 3 papers with code
Cell-Free Multi-User MIMO Equalization via In-Context Learning
In this work, we demonstrate that ICL can be also used to tackle the problem of multi-user equalization in cell-free MIMO systems with limited fronthaul capacity.
Generalization and Informativeness of Conformal Prediction
A popular technique to achieve this goal is conformal prediction (CP), which transforms an arbitrary base predictor into a set predictor with coverage guarantees.
In-Context Learning for MIMO Equalization Using Transformer-Based Sequence Models
In ICL, a decision on a new input is made via a direct mapping of the input and of a few examples from the given task, serving as the task's context, to the output variable.
Forking Uncertainties: Reliable Prediction and Model Predictive Control with Sequence Models via Conformal Risk Control
This property is leveraged to devise a novel model predictive control (MPC) framework that addresses open-loop and closed-loop control problems under general average constraints on the quality or safety of the control policy.
Federated Inference with Reliable Uncertainty Quantification over Wireless Channels via Conformal Prediction
Recent work has introduced federated conformal prediction (CP), which leverages devices-to-server communication to improve the reliability of the server's decision.
User-Centric Federated Learning: Trading off Wireless Resources for Personalization
Statistical heterogeneity across clients in a Federated Learning (FL) system increases the algorithm convergence time and reduces the generalization performance, resulting in a large communication overhead in return for a poor model.
When is Importance Weighting Correction Needed for Covariate Shift Adaptation?
Classic results show that the IW correction is needed when the model is parametric and misspecified.
Robust Bayesian Learning for Reliable Wireless AI: Framework and Applications
In this context, we explore the application of the framework of robust Bayesian learning.
Communication-Efficient Distributionally Robust Decentralized Learning
Decentralized learning algorithms empower interconnected devices to share data and computational resources to collaboratively train a machine learning model without the aid of a central coordinator.
Robust PAC$^m$: Training Ensemble Models Under Misspecification and Outliers
Standard Bayesian learning is known to have suboptimal generalization capabilities under misspecification and in the presence of outliers.
UAV-Aided Decentralized Learning over Mesh Networks
Decentralized learning empowers wireless network devices to collaboratively train a machine learning (ML) model relying solely on device-to-device (D2D) communication.
Asynchronous Decentralized Learning over Unreliable Wireless Networks
Decentralized learning enables edge users to collaboratively train models by exchanging information via device-to-device communication, yet prior works have been limited to wireless networks with fixed topologies and reliable workers.
User-Centric Federated Learning
Data heterogeneity across participating devices poses one of the main challenges in federated learning as it has been shown to greatly hamper its convergence time and generalization capabilities.
LIDAR and Position-Aided mmWave Beam Selection with Non-local CNNs and Curriculum Training
Efficient millimeter wave (mmWave) beam selection in vehicle-to-infrastructure (V2I) communication is a crucial yet challenging task due to the narrow mmWave beamwidth and high user mobility.
Team Deep Mixture of Experts for Distributed Power Control
In the context of wireless networking, it was recently shown that multiple DNNs can be jointly trained to offer a desired collaborative behaviour capable of coping with a broad range of sensing uncertainties.
