Publications associées à Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization

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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

Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization

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Universal and adaptive methods for robust stochastic optimization

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

The statistical complexity of early-stopped mirror descent

Stochastic distributed learning with gradient quantization and double-variance reduction

On The Convergence Of Stochastic Primal-Dual Hybrid Gradient

An Accelerated First-Order Method for Non-convex Optimization on Manifolds

Adaptation in Stochastic Algorithms: From Nonsmooth Optimization to Min-Max Problems and Beyond

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

Convex optimization in sums of Banach spaces

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

The statistical complexity of early-stopped mirror descent

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Universal and adaptive methods for robust stochastic optimization

An Accelerated First-Order Method for Non-convex Optimization on Manifolds

On The Convergence Of Stochastic Primal-Dual Hybrid Gradient

Stochastic distributed learning with gradient quantization and double-variance reduction

Convex optimization in sums of Banach spaces

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

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

Adaptation in Stochastic Algorithms: From Nonsmooth Optimization to Min-Max Problems and Beyond