5 citations · 5 across the 12 of their papers we have counts for
13 papers
A Fundamental Limit in Decentralized Decision-Making
Marco Carpentiero, Felice Scala, Vincenzo Matta +1
In decentralized decision-making, several agents connected according to a network graph aim at solving a classification problem by collecting streaming observations. Due to decentr…
Test-Time Collaborative Classification over Multi-Agent Networks
Ping Hu, Mert Kayaalp, Ali H. Sayed
The increasing heterogeneity of multi-agent systems poses significant challenges for jointly training a global model across agents. At the same time, cooperative inference between…
Performance Evaluation of Social Learning
Felice Scala, Marco Carpentiero, Vincenzo Matta +1
Social Learning is a decentralized decision-making paradigm in which spatially dispersed agents collect streaming observations regulated by one of a finite number of models (the hy…
Opinion Consensus Formation Among Networked Large Language Models
Iris Yazici, Mert Kayaalp, Stefan Taga +1
Can classical consensus models predict the group behavior of large language models (LLMs)? We examine multi-round interactions among LLM agents through the DeGroot framework, where…
Policy Optimization in Multi-Agent Settings under Partially Observable Environments
Ainur Zhaikhan, Malek Khammassi, Ali H. Sayed
This work leverages adaptive social learning to estimate partially observable global states in multi-agent reinforcement learning (MARL) problems. Unlike existing methods, the prop…
Doubly Adaptive Social Learning
Marco Carpentiero, Virginia Bordignon, Vincenzo Matta +1
In social learning, a network of agents assigns probability scores (beliefs) to some hypotheses of interest, which rule the generation of local streaming data observed by each agen…
Multi-agent Off-policy Actor-Critic Reinforcement Learning for Partially Observable Environments
Ainur Zhaikhan, Ali H. Sayed
This study proposes the use of a social learning method to estimate a global state within a multi-agent off-policy actor-critic algorithm for reinforcement learning (RL) operating…
Differential error feedback for communication-efficient decentralized learning
Roula Nassif, Stefan Vlaski, Marco Carpentiero +2
Communication-constrained algorithms for decentralized learning and optimization rely on local updates coupled with the exchange of compressed signals. In this context, differentia…
Causal Influence in Federated Edge Inference
Mert Kayaalp, Yunus Inan, Visa Koivunen +1
In this paper, we consider a setting where heterogeneous agents with connectivity are performing inference using unlabeled streaming data. Observed data are only partially informat…
Detection of Malicious Agents in Social Learning
Valentina Shumovskaia, Mert Kayaalp, Ali H. Sayed
Non-Bayesian social learning is a framework for distributed hypothesis testing aimed at learning the true state of the environment. Traditionally, the agents are assumed to receive…
Asynchronous Diffusion Learning with Agent Subsampling and Local Updates
Elsa Rizk, Kun Yuan, Ali H. Sayed
In this work, we examine a network of agents operating asynchronously, aiming to discover an ideal global model that suits individual local datasets. Our assumption is that each ag…
Social Learning in Community Structured Graphs
Valentina Shumovskaia, Mert Kayaalp, Ali H. Sayed
Traditional social learning frameworks consider environments with a homogeneous state, where each agent receives observations conditioned on that true state of nature. In this work…
Non-Asymptotic Performance of Social Machine Learning Under Limited Data
Ping Hu, Virginia Bordignon, Mert Kayaalp +1
This paper studies the probability of error associated with the social machine learning framework, which involves an independent training phase followed by a cooperative decision-m…