Federated learning (FL) is a distributed machine learning framework wher...
Meta-Reinforcement Learning (MRL) is a promising framework for training
...
We study the decentralized optimization problem where a network of n age...
We study the personalized federated learning problem under asynchronous
...
Synchronous updates may compromise the efficiency of cross-device federa...
We study the decentralized consensus and stochastic optimization problem...
We study the acceleration of the Local Polynomial Interpolation-based
Gr...
In this work, we propose an algorithm for solving exact sparse linear
re...
We propose a distributed Quantum State Tomography (QST) protocol, named ...
We consider the distributed stochastic optimization problem where n agen...
Modern machine learning architectures are often highly expressive. They ...
We propose a new decentralized average consensus algorithm with compress...
In this paper, we propose a first-order distributed optimization algorit...
We study the problem of distributed cooperative learning, where a group ...
This paper studies the problem of distributed classification with a netw...
We consider the distributed learning problem where a network of n agents...
We consider the model of cooperative learning via distributed non-Bayesi...
We propose a distributed, cubic-regularized Newton method for large-scal...
Many problems in statistical learning, imaging, and computer vision invo...
We study the problem of non-Bayesian social learning with uncertain mode...
Non-Bayesian social learning theory provides a framework that models
dis...
We study the convergence of the log-linear non-Bayesian social learning
...
Opinion formation cannot be modeled solely as an ideological deduction f...
We study the optimal convergence rates for distributed convex optimizati...
We study the problem of decentralized distributed computation of a discr...
This paper studies an acceleration technique for incremental aggregated
...
We propose a new class-optimal algorithm for the distributed computation...
In this paper, we study the optimal convergence rate for distributed con...
We study the problem of cooperative inference where a group of agents
in...
We overview some results on distributed learning with focus on a family ...
A recent algorithmic family for distributed optimization, DIGing's, have...