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

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Proximal Point Imitation Learning

Yapa: Accelerated Proximal Algorithm For Convex Composite Problems

Stochastic homogenization of degenerate integral functionals with linear growth

Federated Learning Over Wireless Networks: Convergence Analysis and Resource Allocation

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

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

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