Publications associées à Stochastic Three-Composite Convex Minimization with a Linear Operator

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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

Stochastic Three-Composite Convex Minimization with a Linear Operator

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Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Stochastic homogenization of degenerate integral functionals with linear growth

Stochastic distributed learning with gradient quantization and double-variance reduction

Proximal Point Imitation Learning

On The Convergence Of Stochastic Primal-Dual Hybrid Gradient

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

Yapa: Accelerated Proximal Algorithm For Convex Composite Problems

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

Federated Learning Over Wireless Networks: Convergence Analysis and Resource Allocation

Fast Projection Onto Convex Smooth Constraints

Stochastic homogenization of degenerate integral functionals with linear growth

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Proximal Point Imitation Learning

On The Convergence Of Stochastic Primal-Dual Hybrid Gradient

Fast Projection Onto Convex Smooth Constraints

Stochastic distributed learning with gradient quantization and double-variance reduction

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

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

Federated Learning Over Wireless Networks: Convergence Analysis and Resource Allocation

Yapa: Accelerated Proximal Algorithm For Convex Composite Problems