Publications associées à Intelligence artificielle distribuée

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Distributed Coupled Multiagent Stochastic Optimization

Ali H. Sayed, Sulaiman A S A E Alghunaim

This paper develops an effective distributed strategy for the solution of constrained multiagent stochastic optimization problems with coupled parameters across the agents. In this formulation, each agent is influenced by only a subset of the entries of a ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Linear convergence of primal-dual gradient methods and their performance in distributed optimization

Ali H. Sayed, Sulaiman A S A E Alghunaim

In this work, we revisit a classical incremental implementation of the primal-descent dual-ascent gradient method used for the solution of equality constrained optimization problems. We provide a short proof that establishes the linear (exponential) conver ...

PERGAMON-ELSEVIER SCIENCE LTD2020

Lyrics-Conditioned Neural Melody Generation

Simon Canales, Yi Yu

Generating melody from lyrics to compose a song has been a very interesting research topic in the area of artificial intelligence and music, which tries to predict generative music relationship between lyrics and melody. In this demonstration paper, by exp ...

SPRINGER INTERNATIONAL PUBLISHING AG2020

On Maintaining Linear Convergence of Distributed Learning and Optimization Under Limited Communication

Na Li, Hossein Shokri Ghadikolaei

In distributed optimization and machine learning, multiple nodes coordinate to solve large problems. To do this, the nodes need to compress important algorithm information to bits so that it can be communicated over a digital channel. The communication tim ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Robust P2P Personalized Learning

Rachid Guerraoui, Yahya Benkaouz

Decentralized machine learning over peer-to-peer networks is very appealing for it enables to learn personalized models without sharing users data, nor relying on any central server. Peers can improve upon their locally trained model across a network graph ...

IEEE2020

Second-Order Guarantees In Federated Learning

Ali H. Sayed, Stefan Vlaski, Elsa Rizk

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 ...

IEEE2020

Optimal target assignment for massive spectroscopic surveys

Denis Gillet, Jean-Paul Richard Kneib, Matin Macktoobian

Robotics have recently contributed to cosmological spectroscopy to automatically obtain the map of the observable universe using robotic fiber positioners. For this purpose, an assignment algorithm is required to assign each robotic fiber positioner to a t ...

2020

Distributed Learning over Networks under Subspace Constraints

Ali H. Sayed, Stefan Vlaski, Roula Nassif

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 ...

IEEE2019

Intelligence artificielle distribuée

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Distributed Coupled Multiagent Stochastic Optimization

Learning the Globally Optimal Distributed LQ Regulator

Linear convergence of primal-dual gradient methods and their performance in distributed optimization

Generating Higher-Fidelity Synthetic Datasets with Privacy Guarantees

Lyrics-Conditioned Neural Melody Generation

On Maintaining Linear Convergence of Distributed Learning and Optimization Under Limited Communication

Robust P2P Personalized Learning

Second-Order Guarantees In Federated Learning

Optimal target assignment for massive spectroscopic surveys

Distributed Learning over Networks under Subspace Constraints

Learning the Globally Optimal Distributed LQ Regulator

Optimal target assignment for massive spectroscopic surveys

Lyrics-Conditioned Neural Melody Generation

Distributed Coupled Multiagent Stochastic Optimization

Linear convergence of primal-dual gradient methods and their performance in distributed optimization

Generating Higher-Fidelity Synthetic Datasets with Privacy Guarantees

On Maintaining Linear Convergence of Distributed Learning and Optimization Under Limited Communication

Robust P2P Personalized Learning

Second-Order Guarantees In Federated Learning

Distributed Learning over Networks under Subspace Constraints