no code implementations • 17 Apr 2024 • Qiang Li, Michal Yemini, Hoi-To Wai
This paper studies the convergence properties of these algorithms in a performative prediction setting, where the data distribution may shift due to the deployed prediction model.
no code implementations • 28 Feb 2023 • Rajarshi Saha, Mohamed Seif, Michal Yemini, Andrea J. Goldsmith, H. Vincent Poor
This work considers the problem of Distributed Mean Estimation (DME) over networks with intermittent connectivity, where the goal is to learn a global statistic over the data samples localized across distributed nodes with the help of a central server.
no code implementations • 6 Sep 2022 • Nir Weinberger, Michal Yemini
Additionally, under the assumption that the \textit{exact} alphabet size is unknown, and instead the player only knows a loose upper bound on it, a UCB-based algorithm is proposed, in which the player aims to reduce the regret caused by the unknown alphabet size in a finite time regime.
no code implementations • 23 May 2022 • Michal Yemini, Rajarshi Saha, Emre Ozfatura, Deniz Gündüz, Andrea J. Goldsmith
We present a semi-decentralized federated learning algorithm wherein clients collaborate by relaying their neighbors' local updates to a central parameter server (PS).
no code implementations • 28 Feb 2022 • Tomer Gafni, Michal Yemini, Kobi Cohen
Motivated by recent studies on related RMAB settings, the regret is defined as the reward loss with respect to a player that knows the dynamics of the problem, and plays at each time t the arm that maximizes the expected immediate value.
no code implementations • 24 Feb 2022 • Michal Yemini, Rajarshi Saha, Emre Ozfatura, Deniz Gündüz, Andrea J. Goldsmith
Intermittent connectivity of clients to the parameter server (PS) is a major bottleneck in federated edge learning frameworks.
no code implementations • 17 Dec 2021 • Tomer Gafni, Michal Yemini, Kobi Cohen
Motivated by recent studies on related RMAB settings, the regret is defined as the reward loss with respect to a player that knows the dynamics of the problem, and plays at each time $t$ the arm that maximizes the expected immediate value.
no code implementations • 3 Oct 2021 • Michal Yemini, Stephanie Gil, Andrea J. Goldsmith
The connectivity of each sensor cluster is intermittent and depends on the available communication opportunities of the sensors to the fusion center.
no code implementations • 9 Mar 2021 • Michal Yemini, Angelia Nedić, Andrea Goldsmith, Stephanie Gil
Further, the expected convergence rate decays exponentially with the quality of the trust observations between agents.
Optimization and Control Robotics Systems and Control Signal Processing Systems and Control
no code implementations • 30 Oct 2020 • Michal Yemini, Elza Erkip, Andrea J. Goldsmith
Our numerical results show that our scheme decreases the number of users in the system whose rate falls below the guaranteed rate, set to $128$kbps, $256$kbps or $512$kbps, when compared with our previously proposed optimization methods.
no code implementations • 26 May 2020 • Michal Yemini, Stephanie Gil, Andrea Goldsmith
We refer to this hybrid communication scheme as a cloud-cluster architecture.
no code implementations • 22 Oct 2019 • Michal Yemini, Amir Leshem, Anelia Somekh-Baruch
Furthermore, we assume structural side information where the decision maker knows in advance that there are two types of hidden states; one is common to all arms and evolves according to a Markovian distribution, and the other is unique to each arm and is distributed according to an i. i. d.