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Found 13 papers, 3 papers with code
Non-Asymptotic Performance of Social Machine Learning Under Limited Data
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.
Memory-Aware Social Learning under Partial Information Sharing
We devise a novel learning strategy where each agent forms a valid belief by completing the partial beliefs received from its neighbors.
Distributed Bayesian Learning of Dynamic States
This work studies networked agents cooperating to track a dynamical state of nature under partial information.
Dencentralized learning in the presence of low-rank noise
Observations collected by agents in a network may be unreliable due to observation noise or interference.
Optimal Aggregation Strategies for Social Learning over Graphs
Adaptive social learning is a useful tool for studying distributed decision-making problems over graphs.
Social Opinion Formation and Decision Making Under Communication Trends
We show that agents can learn the true hypothesis even if they do not discuss it, at rates comparable to traditional social learning.
Learning from Heterogeneous Data Based on Social Interactions over Graphs
In the proposed social machine learning (SML) strategy, two phases are present: in the training phase, classifiers are independently trained to generate a belief over a set of hypotheses using a finite number of training samples; in the prediction phase, classifiers evaluate streaming unlabeled observations and share their instantaneous beliefs with neighboring classifiers.
Hidden Markov Modeling over Graphs
This work proposes a multi-agent filtering algorithm over graphs for finite-state hidden Markov models (HMMs), which can be used for sequential state estimation or for tracking opinion formation over dynamic social networks.
Deception in Social Learning
Then, we will explain that it is possible for such attacks to succeed by showing that strategies exist that can be adopted by the malicious agents for this purpose.
Social learning under inferential attacks
A common assumption in the social learning literature is that agents exchange information in an unselfish manner.
Network Classifiers Based on Social Learning
Combination over time means that the classifiers respond to streaming data during testing and continue to improve their performance even during this phase.
Partial Information Sharing over Social Learning Networks
Instead of sharing the entirety of their beliefs, this work considers the case in which agents will only share their beliefs regarding one hypothesis of interest, with the purpose of evaluating its validity, and draws conditions under which this policy does not affect truth learning.
Social Learning with Partial Information Sharing
This work studies the learning abilities of agents sharing partial beliefs over social networks.
Signal Processing Multiagent Systems
