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09/14/2023
Deep Multi-Agent Reinforcement Learning for Decentralized Active Hypothesis Testing
We consider a decentralized formulation of the active hypothesis testing...
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03/27/2023
Multi-Flow Transmission in Wireless Interference Networks: A Convergent Graph Learning Approach
We consider the problem of of multi-flow transmission in wireless networ...
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03/18/2023
Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approach
Federated learning (FL) is an emerging machine learning (ML) paradigm us...
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01/21/2023
A Communication-Efficient Adaptive Algorithm for Federated Learning under Cumulative Regret
We consider the problem of online stochastic optimization in a distribut...
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03/22/2022
An Online Learning Approach to Shortest Path and Backpressure Routing in Wireless Networks
We consider the adaptive routing problem in multihop wireless networks. ...
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02/28/2022
Restless Multi-Armed Bandits under Exogenous Global Markov Process
We consider an extension to the restless multi-armed bandit (RMAB) probl...
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02/21/2022
Composite Anomaly Detection via Hierarchical Dynamic Search
Anomaly detection among a large number of processes arises in many appli...
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12/17/2021
Learning in Restless Bandits under Exogenous Global Markov Process
We consider an extension to the restless multi-armed bandit (RMAB) probl...
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10/24/2021
Deep Reinforcement Learning for Simultaneous Sensing and Channel Access in Cognitive Networks
We consider the problem of dynamic spectrum access (DSA) in cognitive wi...
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08/06/2021
Anomaly Search with Multiple Plays under Delay and Switching Costs
The problem of searching for L anomalous processes among M processes is ...
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07/26/2021
Accelerated Gradient Descent Learning over Multiple Access Fading Channels
We consider a distributed learning problem in a wireless network, consis...
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03/31/2021
Federated Learning: A Signal Processing Perspective
The dramatic success of deep learning is largely due to the availability...
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09/27/2020
Over-the-Air Federated Learning from Heterogeneous Data
Federated learning (FL) is a framework for distributed learning of centr...
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01/14/2020
PoPS: Policy Pruning and Shrinking for Deep Reinforcement Learning
The recent success of deep neural networks (DNNs) for function approxima...
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11/13/2019
Searching for Anomalies over Composite Hypotheses
The problem of detecting anomalies in multiple processes is considered. ...
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08/20/2019
On Analog Gradient Descent Learning over Multiple Access Fading Channels
We consider a distributed learning problem over multiple access channel ...
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06/19/2019
Learning in Restless Multi-Armed Bandits via Adaptive Arm Sequencing Rules
We consider a class of restless multi-armed bandit (RMAB) problems with ...
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09/22/2018
Spectrum and Energy Efficient Multiple Access for Detection in Wireless Sensor Networks
We consider a binary hypothesis testing problem using Wireless Sensor Ne...
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08/04/2018