220 citations · 836 across the 35 of their papers we have counts for
36 papers
Distributed Learning in Markovian Restless Bandits over Interference Graphs for Stable Spectrum Sharing
Liad Lea Didi, Kobi Cohen
We study distributed learning for spectrum access and sharing among multiple cognitive communication entities, such as cells, subnetworks, or cognitive radio users (collectively re…
Control-Optimized Deep Reinforcement Learning for Artificially Intelligent Autonomous Systems
Oren Fivel, Matan Rudman, Kobi Cohen
Deep reinforcement learning (DRL) has become a powerful tool for complex decision-making in machine learning and AI. However, traditional methods often assume perfect action execut…
Searching for a Hidden Markov Anomaly over Multiple Processes
Levli Citron, Kobi Cohen, Qing Zhao
We address the problem of detecting an anomalous process among a large number of processes. At each time t, normal processes are in state zero (normal state), while the abnormal pr…
Asymptotically Optimal Search for a Change Point Anomaly under a Composite Hypothesis Model
Liad Lea Didi, Tomer Gafni, Kobi Cohen
We address the problem of searching for a change point in an anomalous process among a finite set of M processes. Specifically, we address a composite hypothesis model in which eac…
SINR-Aware Deep Reinforcement Learning for Distributed Dynamic Channel Allocation in Cognitive Interference Networks
Yaniv Cohen, Tomer Gafni, Ronen Greenberg +1
We consider the problem of dynamic channel allocation (DCA) in cognitive communication networks with the goal of maximizing a global signal-to-interference-plus-noise ratio (SINR)…
Deep Multi-Agent Reinforcement Learning for Decentralized Active Hypothesis Testing
Hadar Szostak, Kobi Cohen
We consider a decentralized formulation of the active hypothesis testing (AHT) problem, where multiple agents gather noisy observations from the environment with the purpose of ide…
Federated Learning from Heterogeneous Data via Controlled Bayesian Air Aggregation
Tomer Gafni, Kobi Cohen, Yonina C. Eldar
Federated learning (FL) is an emerging machine learning paradigm for training models across multiple edge devices holding local data sets, without explicitly exchanging the data. R…
Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approach
Dan Ben Ami, Kobi Cohen, Qing Zhao
Federated learning (FL) is an emerging machine learning (ML) paradigm used to train models across multiple nodes (i.e., clients) holding local data sets, without explicitly exchang…
Multi-Flow Transmission in Wireless Interference Networks: A Convergent Graph Learning Approach
Raz Paul, Kobi Cohen, Gil Kedar
We consider the problem of of multi-flow transmission in wireless networks, where data signals from different flows can interfere with each other due to mutual interference between…
A Communication-Efficient Adaptive Algorithm for Federated Learning under Cumulative Regret
Sudeep Salgia, Qing Zhao, Tamir Gabay +1
We consider the problem of online stochastic optimization in a distributed setting with clients connected through a central server. We develop a distributed online learning alg…
Topological transitions and surface umklapp scattering in Slack Metasurfaces
Kobi-Yaakov Cohen, Shai Tsesses, Shimon Dolev +3
Metamaterials and metasurfaces are at the pinnacle of wave propagation engineering, yet their design has thus far been mainly focused on deep-subwavelength periodicities, practical…
An Online Learning Approach to Shortest Path and Backpressure Routing in Wireless Networks
Omer Amar, Kobi Cohen
We consider the adaptive routing problem in multihop wireless networks. The link states are assumed to be random variables drawn from unknown distributions, independent and identic…
Restless Multi-Armed Bandits under Exogenous Global Markov Process
Tomer Gafni, Michal Yemini, Kobi Cohen
We consider an extension to the restless multi-armed bandit (RMAB) problem with unknown arm dynamics, where an unknown exogenous global Markov process governs the rewards distribut…
Anomaly Search over Composite Hypotheses in Hierarchical Statistical Models
Benjamin Wolff, Tomer Gafni, Guy Revach +2
Detection of anomalies among a large number of processes is a fundamental task that has been studied in multiple research areas, with diverse applications spanning from spectrum ac…
Learning in Restless Bandits under Exogenous Global Markov Process
Tomer Gafni, Michal Yemini, Kobi Cohen
We consider an extension to the restless multi-armed bandit (RMAB) problem with unknown arm dynamics, where an unknown exogenous global Markov process governs the rewards distribut…
Deep Reinforcement Learning for Simultaneous Sensing and Channel Access in Cognitive Networks
Yoel Bokobza, Ron Dabora, Kobi Cohen
We consider the problem of dynamic spectrum access (DSA) in cognitive wireless networks, where only partial observations are available to the users due to narrowband sensing and tr…
Anomaly Search with Multiple Plays under Delay and Switching Costs
Tidhar Lambez, Kobi Cohen
The problem of searching for anomalous processes among processes is considered. At each time, the decision maker can observe a subset of processes (i.e., multiple plays…
Accelerated Gradient Descent Learning over Multiple Access Fading Channels
Raz Paul, Yuval Friedman, Kobi Cohen
We consider a distributed learning problem in a wireless network, consisting of N distributed edge devices and a parameter server (PS). The objective function is a sum of the edge…
Federated Learning: A Signal Processing Perspective
Tomer Gafni, Nir Shlezinger, Kobi Cohen +2
The dramatic success of deep learning is largely due to the availability of data. Data samples are often acquired on edge devices, such as smart phones, vehicles and sensors, and i…
Distributed Learning over Markovian Fading Channels for Stable Spectrum Access
Tomer Gafni, Kobi Cohen
We consider the problem of multi-user spectrum access in wireless networks. The bandwidth is divided into K orthogonal channels, and M users aim to access the spectrum. Each user c…
Over-the-Air Federated Learning from Heterogeneous Data
Tomer Sery, Nir Shlezinger, Kobi Cohen +1
Federated learning (FL) is a framework for distributed learning of centralized models. In FL, a set of edge devices train a model using their local data, while repeatedly exchangin…
PoPS: Policy Pruning and Shrinking for Deep Reinforcement Learning
Dor Livne, Kobi Cohen
The recent success of deep neural networks (DNNs) for function approximation in reinforcement learning has triggered the development of Deep Reinforcement Learning (DRL) algorithms…
Searching for Anomalies over Composite Hypotheses
Bar Hemo, Tomer Gafni, Kobi Cohen +1
The problem of detecting anomalies in multiple processes is considered. We consider a composite hypothesis case, in which the measurements drawn when observing a process follow a c…
On Analog Gradient Descent Learning over Multiple Access Fading Channels
Tomer Sery, Kobi Cohen
We consider a distributed learning problem over multiple access channel (MAC) using a large wireless network. The computation is made by the network edge and is based on received d…