Publications de Stefan Vlaski | EPFL Graph Search

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This work proposes a decentralized architecture, where individual agents aim at solving a classification problem while observing streaming features of different dimensions and arising from possibly different distributions. In the context of social learning ...

This paper considers optimization problems over networks where agents have individual objectives to meet, or individual parameter vectors to estimate, subject to subspace constraints that require the objectives across the network to lie in low-dimensional ...

In this article, 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. This constrained fo ...

This work proposes a new way of combining independently trained classifiers over space and time. Combination over space means that the outputs of spatially distributed classifiers are aggregated. Combination over time means that the classifiers respond to ...

Part I of this paper formulated a multitask optimization problem where agents in the network have individual objectives to meet, or individual parameter vectors to estimate, subject to a smoothness condition over the graph. A diffusion strategy was devised ...

In this paper we study the problem of social learning under multiple true hypotheses and self-interested agents that exchange information over a graph. In this setup, each agent receives data that might be generated from a different hypothesis (or state) t ...

This work presents and studies a distributed algorithm for solving optimization problems over networks where agents have individual costs to minimize subject to subspace constraints that require the minimizers across the network to lie in a low-dimensional ...

Federated learning has emerged as an umbrella term for centralized coordination strategies in multi-agent environments. While many federated learning architectures process data in an online manner, and are hence adaptive by nature, most performance analyse ...

Recent years have seen increased interest in performance guarantees of gradient descent algorithms for nonconvex optimization. A number of works have uncovered that gradient noise plays a critical role in the ability of gradient descent recursions to effic ...

Driven by the need to solve increasingly complex optimization problems in signal processing and machine learning, recent years have seen rising interest in the behavior of gradient-descent based algorithms in non-convex environments. Most of the works on d ...

Federated learning is a semi-distributed algorithm, where a server communicates with multiple dispersed clients to learn a global model. The federated architecture is not robust and is sensitive to communication and computational overloads due to its one-m ...

The diffusion strategy for distributed learning from streaming data employs local stochastic gradient updates along with exchange of iterates over neighborhoods. In this work we establish that agents cluster around a network centroid in the mean-fourth sen ...

The objective of meta-learning is to exploit knowledge obtained from observed tasks to improve adaptation to unseen tasks. Meta-learners are able to generalize better when they are trained with a larger number of observed tasks and with a larger amount of ...

Federated learning is a useful framework for centralized learning from distributed data under practical considerations of heterogeneity, asynchrony, and privacy. Federated architectures are frequently deployed in deep learning settings, which generally giv ...

The diffusion strategy for distributed learning from streaming data employs local stochastic gradient updates along with exchange of iterates over neighborhoods. In Part I [3] of this work we established that agents cluster around a network centroid and pr ...

Social learning algorithms provide models for the formation of opinions over social networks resulting from local reasoning and peer-to-peer exchanges. Interactions occur over an underlying graph topology, which describes the flow of information among the ...

This paper formulates a multitask optimization problem where agents in the network have individual objectives to meet, or individual parameter vectors to estimate, subject to a smoothness condition over the graph. The smoothness condition softens the trans ...

Rapid advances in data collection and processing capabilities have allowed for the use of increasingly complex models that give rise to nonconvex optimization problems. These formulations, however, can be arbitrarily difficult to solve in general, in the s ...

Part & x00A0;I of this paper considered optimization problems over networks where agents have individual objectives to meet, or individual parameter vectors to estimate, subject to subspace constraints that require the objectives across the network to lie ...

This letter proposes a general regularization framework for inference over multitask networks. The optimization approach relies on minimizing a global cost consisting of the aggregate sum of individual costs regularized by a term that allows to incorporate ...

Stefan Vlaski

Chattez avec Graph Search

Learning From Heterogeneous Data Based on Social Interactions Over Graphs

Adaptation and Learning Over Networks Under Subspace Constraints-Part I: Stability Analysis

Quantization for Decentralized Learning Under Subspace Constraints

Network Classifiers Based On Social Learning

Learning Over Multitask Graphs-Part II: Performance Analysis

Self-Aware Social Learning Over Graphs

Distributed Learning over Networks under Subspace Constraints

Dynamic Federated Learning

Second-Order Guarantees of Stochastic Gradient Descent in Nonconvex Optimization

Diffusion Learning In Non-Convex Environments

Privatized graph federated learning

Polynomial Escape-Time From Saddle Points In Distributed Non-Convex Optimization

Dif-MAML: Decentralized Multi-Agent Meta-Learning

Second-Order Guarantees In Federated Learning

Distributed Learning in Non-Convex Environments-Part II: Polynomial Escape From Saddle-Points

Explainability and Graph Learning From Social Interactions

Learning Over Multitask Graphs-Part I: Stability Analysis

Second-order guarantees in centralized, federated and decentralized nonconvex optimization

Adaptation and Learning Over Networks Under Subspace Constraints & x2014;Part II: Performance Analysis

A Regularization Framework for Learning Over Multitask Graphs