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Found 11 papers, 3 papers with code
Compressed Regression over Adaptive Networks
In this work we derive the performance achievable by a network of distributed agents that solve, adaptively and in the presence of communication constraints, a regression problem.
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.
Quantization for decentralized learning under subspace constraints
In this paper, we consider decentralized optimization problems where agents have individual cost functions to minimize subject to subspace constraints that require the minimizers across the network to lie in low-dimensional subspaces.
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.
Distributed Adaptive Learning Under Communication Constraints
We propose a diffusion strategy nicknamed as ACTC (Adapt-Compress-Then-Combine), which relies on the following steps: i) an adaptation step where each agent performs an individual stochastic-gradient update with constant step-size; ii) a compression step that leverages a recently introduced class of stochastic compression operators; and iii) a combination step where each agent combines the compressed updates received from its neighbors.
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.
Graph Learning Under Partial Observability
Many optimization, inference and learning tasks can be accomplished efficiently by means of decentralized processing algorithms where the network topology (i. e., the graph) plays a critical role in enabling the interactions among neighboring nodes.
Social Learning with Partial Information Sharing
This work studies the learning abilities of agents sharing partial beliefs over social networks.
Signal Processing Multiagent Systems
Graph Learning over Partially Observed Diffusion Networks: Role of Degree Concentration
This claim is proved for three matrix estimators: i) the Granger estimator that adapts to the partial observability setting the solution that is exact under full observability ; ii) the one-lag correlation matrix; and iii) the residual estimator based on the difference between two consecutive time samples.
