Mert Kayaalp

Optimal Combination Policies for Error Exponent Maximization in Social Learning

Distributed decision-making over networks involves multiple agents collaborating to achieve a common goal. In the social learning process, where agents aim at inferring an unknown state from a stream of local observations, the probability of error in their ...

IEEE2025

Non-asymptotic performance of social machine learning under limited data

Ali H. Sayed, Virginia Bordignon, Mert Kayaalp, Ping Hu

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-making phase over a graph. This framework addresses the problem of classifyin ...

2025

Social Learning in Community Structured Graphs

Ali H. Sayed, Mert Kayaalp, Valentina Shumovskaia

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, we relax this assumption and study the distributed hypothesis testing prob ...

2024

Causal Influence in Federated Edge Inference

Ali H. Sayed, Mert Kayaalp, Yunus Inan

In this paper, we consider a setting where heterogeneous agents with connectivity are performing inference using unlabeled streaming data. Observed data are only partially informative about the target variable of interest. In order to overcome the uncertai ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2024

On the Arithmetic and Geometric Fusion of Beliefs for Distributed Inference

Ali H. Sayed, Emre Telatar, Mert Kayaalp, Yunus Inan

We study the asymptotic learning rates of belief vectors in a distributed hypothesis testing problem under linear and log-linear combination rules. We show that under both combination strategies, agents are able to learn the truth exponentially fast, with ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Detection of Malicious Agents in Social Learning

Ali H. Sayed, Mert Kayaalp, Valentina Shumovskaia

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 observations conditioned on the same true state, although it is also possib ...

IEEE Institute of Electrical and Electronics Engineers2024

Social Opinion Formation and Decision Making Under Communication Trends

Ali H. Sayed, Virginia Bordignon, Mert Kayaalp

This work studies the learning process over social networks under partial and random information sharing. In traditional social learning models, agents exchange full belief information with each other while trying to infer the true state of nature. We stud ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Causal Impact Analysis for Asynchronous Decision Making

Ali H. Sayed, Mert Kayaalp, Yunus Inan

We consider a collaborative decision-making framework where heterogeneous agents receive streaming and partially informative observations. We consider two asynchronous scenarios that differ based on the agents' participation patterns and the fusion center' ...

IEEE2024

Distributed Decision-Making for Community Structured Networks

Ali H. Sayed, Mert Kayaalp, Valentina Shumovskaia

Traditional social learning frameworks consider environments with a homogeneous state where each agent receives observations conditioned on the same hypothesis. In this work, we study the distributed hypothesis testing problem for graphs with a community s ...

Institute of Electrical and Electronics Engineers Inc.2024

Mert Kayaalp

Graph Chatbot

Sequential Reasoning with Socially Caused Beliefs

Optimal Combination Policies for Error Exponent Maximization in Social Learning

Non-asymptotic performance of social machine learning under limited data

Social Learning in Community Structured Graphs

Causal Influence in Federated Edge Inference

On the Arithmetic and Geometric Fusion of Beliefs for Distributed Inference

Detection of Malicious Agents in Social Learning

Social Opinion Formation and Decision Making Under Communication Trends

Causal Impact Analysis for Asynchronous Decision Making

Distributed Decision-Making for Community Structured Networks

Causal Influence in Federated Edge Inference

On the Arithmetic and Geometric Fusion of Beliefs for Distributed Inference

Social Opinion Formation and Decision Making Under Communication Trends

Causal Impact Analysis for Asynchronous Decision Making

Optimal Combination Policies for Error Exponent Maximization in Social Learning

Sequential Reasoning with Socially Caused Beliefs

Non-asymptotic performance of social machine learning under limited data

Social Learning in Community Structured Graphs

Detection of Malicious Agents in Social Learning

Distributed Decision-Making for Community Structured Networks