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

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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

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

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Chattez avec Graph Search

Understanding generalization and robustness in modern deep learning

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 homogenization of degenerate integral functionals with linear growth

Stochastic distributed learning with gradient quantization and double-variance reduction

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

Metrizing Fairness

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

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

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

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

Stochastic homogenization of degenerate integral functionals with linear growth

Understanding generalization and robustness in modern deep learning

Universal and adaptive methods for robust stochastic optimization

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

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

Metrizing Fairness

Stochastic distributed learning with gradient quantization and double-variance reduction

The statistical complexity of early-stopped mirror descent