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Amir Daneshmand

2 accepted papers

2021

Newton Method over Networks is Fast up to the Statistical Precision

ICML 2021spotlight

We propose a distributed cubic regularization of the Newton method for solving (constrained) empirical risk minimization problems over a network of agents, modeled as undirected graph. The algorithm employs an inexact, preconditioned Newton step at each agent’s side: the gradient of the centralized…

Cited by 23SourcePDFScholar
2017

D2L: Decentralized dictionary learning over dynamic networks

ICASSP 2017accepted

The paper studies a general class of distributed dictionary learning (DL) problems where the learning task is distributed over a multi-agent network with (possibly) time-varying (non-symmetric) connectivity. This setting is relevant, for instance, in scenarios where massive amounts of data are not c…

Cited by 0SourceScholar