2 code implementations • 8 Apr 2024 • Matteo Zecchin, Kai Yu, Osvaldo Simeone
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.
no code implementations • 22 Jan 2024 • Matteo Zecchin, Sangwoo Park, Osvaldo Simeone, Fredrik Hellström
A popular technique to achieve this goal is conformal prediction (CP), which transforms an arbitrary base predictor into a set predictor with coverage guarantees.
1 code implementation • 10 Nov 2023 • Matteo Zecchin, Kai Yu, Osvaldo Simeone
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.
no code implementations • 16 Oct 2023 • Matteo Zecchin, Sangwoo Park, Osvaldo Simeone
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.
no code implementations • 8 Aug 2023 • Meiyi Zhu, Matteo Zecchin, Sangwoo Park, Caili Guo, Chunyan Feng, Osvaldo Simeone
Recent work has introduced federated conformal prediction (CP), which leverages devices-to-server communication to improve the reliability of the server's decision.
no code implementations • 25 Apr 2023 • Mohamad Mestoukirdi, Matteo Zecchin, David Gesbert, Qianrui Li
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.
no code implementations • 7 Mar 2023 • Davit Gogolashvili, Matteo Zecchin, Motonobu Kanagawa, Marios Kountouris, Maurizio Filippone
Classic results show that the IW correction is needed when the model is parametric and misspecified.
no code implementations • 1 Jul 2022 • Matteo Zecchin, Sangwoo Park, Osvaldo Simeone, Marios Kountouris, David Gesbert
In this context, we explore the application of the framework of robust Bayesian learning.
no code implementations • 31 May 2022 • Matteo Zecchin, Marios Kountouris, David Gesbert
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.
no code implementations • 3 Mar 2022 • Matteo Zecchin, Sangwoo Park, Osvaldo Simeone, Marios Kountouris, David Gesbert
Standard Bayesian learning is known to have suboptimal generalization capabilities under misspecification and in the presence of outliers.
no code implementations • 2 Mar 2022 • Matteo Zecchin, David Gesbert, Marios Kountouris
Decentralized learning empowers wireless network devices to collaboratively train a machine learning (ML) model relying solely on device-to-device (D2D) communication.
no code implementations • 2 Feb 2022 • Eunjeong Jeong, Matteo Zecchin, Marios Kountouris
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.
no code implementations • 19 Oct 2021 • Mohamad Mestoukirdi, Matteo Zecchin, David Gesbert, Qianrui Li, Nicolas Gresset
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.
1 code implementation • 29 Apr 2021 • Matteo Zecchin, Mahdi Boloursaz Mashhadi, Mikolaj Jankowski, Deniz Gunduz, Marios Kountouris, David Gesbert
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.
no code implementations • 28 Jul 2020 • Matteo Zecchin, David Gesbert, Marios Kountouris
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.