Publications associées à Courbe convexe

Stochastic distributed learning with gradient quantization and double-variance reduction

Sebastian Urban Stich, Konstantin Mishchenko

We consider distributed optimization over several devices, each sending incremental model updates to a central server. This setting is considered, for instance, in federated learning. Various schemes have been designed to compress the model updates in orde ...

TAYLOR & FRANCIS LTD2022

Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates

Martin Jaggi, Amirkeivan Mohtashami, Sebastian Urban Stich

It has been experimentally observed that the efficiency of distributed training with stochastic gradient (SGD) depends decisively on the batch size and—in asynchronous implementations—on the gradient staleness. Especially, it has been observed that the spe ...

2021

On the Equivalence of Youla, System-Level, and Input-Output Parameterizations

Maryam Kamgarpour, Luca Furieri, Yu Zheng

A convex parameterization of internally stabilizing controllers is fundamental for many controller synthesis procedures. The celebrated Youla parameterization relies on a doubly coprime factorization of the system, while the recent system-level and input-o ...

2021

A first-order primal-dual method with adaptivity to local smoothness

Volkan Cevher, Maria-Luiza Vladarean

We consider the problem of finding a saddle point for the convex-concave objective

\min_x \max_y f(x) + \langle Ax, y\rangle - g^*(y)

, where

f

is a convex function with locally Lipschitz gradient and

g

is convex and possibly non-smooth. We propose an ...

2021

Federated Learning Over Wireless Networks: Convergence Analysis and Resource Allocation

Vincent Gramoli

There is an increasing interest in a fast-growing machine learning technique called Federated Learning (FL), in which the model training is distributed over mobile user equipment (UEs), exploiting UEs' local computation and training data. Despite its advan ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

A Multi-Agent Primal-Dual Strategy for Composite Optimization over Distributed Features

Ali H. Sayed, Sulaiman A S A E Alghunaim, Ming Yan

This work studies multi-agent sharing optimization problems with the objective function being the sum of smooth local functions plus a convex (possibly non-smooth) function coupling all agents. This scenario arises in many machine learning and engineering ...

IEEE2020

System-level, Input-output and New Parameterizations of Stabilizing Controllers, and Their Numerical Computation

Maryam Kamgarpour, Luca Furieri, Na Li

It is known that the set of internally stabilizing controller

\mathcal{C}_{\text{stab}}

is non-convex, but it admits convex characterizations using certain closed-loop maps: a classical result is the {Youla parameterization}, and two recent notions are t ...

2020

On the convergence of stochastic primal-dual hybrid gradient

Volkan Cevher, Ahmet Alacaoglu

In this paper, we analyze the recently proposed stochastic primal-dual hybrid gradient (SPDHG) algorithm and provide new theoretical results. In particular, we prove almost sure convergence of the iterates to a solution and linear convergence with standard ...

2019

Safe Convex Learning under Uncertain Constraints

Maryam Kamgarpour, Andreas Krause, Ilnura Usmanova

We address the problem of minimizing a convex smooth function f(x) over a compact polyhedral set D given a stochastic zeroth-order constraint feedback model. This problem arises in safety-critical machine learning applications, such as personalized medicin ...

PMLR2019

Relaxed Wyner's Common Information

Michael Christoph Gastpar, Erixhen Sula

In the problem of coded caching for media delivery, two separate coding opportunities have been identified. The first opportunity is a multi-user advantage and crucially hinges on a public broadcast link in the delivery phase. This has been explored in a p ...

IEEE2019

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Stochastic distributed learning with gradient quantization and double-variance reduction

Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates

On the Equivalence of Youla, System-Level, and Input-Output Parameterizations

A first-order primal-dual method with adaptivity to local smoothness

Federated Learning Over Wireless Networks: Convergence Analysis and Resource Allocation

A Multi-Agent Primal-Dual Strategy for Composite Optimization over Distributed Features

System-level, Input-output and New Parameterizations of Stabilizing Controllers, and Their Numerical Computation

On the convergence of stochastic primal-dual hybrid gradient

Safe Convex Learning under Uncertain Constraints

Relaxed Wyner's Common Information

A first-order primal-dual method with adaptivity to local smoothness

Safe Convex Learning under Uncertain Constraints

Federated Learning Over Wireless Networks: Convergence Analysis and Resource Allocation

Stochastic distributed learning with gradient quantization and double-variance reduction

A Multi-Agent Primal-Dual Strategy for Composite Optimization over Distributed Features

System-level, Input-output and New Parameterizations of Stabilizing Controllers, and Their Numerical Computation

Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates

On the Equivalence of Youla, System-Level, and Input-Output Parameterizations

Relaxed Wyner's Common Information

On the convergence of stochastic primal-dual hybrid gradient