Publications associées à Fast Proximal algorithms for Self-concordant function minimization with application to sparse graph selection

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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

Fast Proximal algorithms for Self-concordant function minimization with application to sparse graph selection

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Universal and adaptive methods for robust stochastic optimization

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

UNDERGRAD: A Universal Black-Box Optimization Method with Almost Dimension-Free Convergence Rate Guarantees

STORM+: Fully Adaptive SGD with Momentum for Nonconvex Optimization

A Newton Frank-Wolfe method for constrained self-concordant minimization

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

Regret Minimization in Stochastic Non-Convex Learning via a Proximal-Gradient Approach

Large Steps in Inverse Rendering of Geometry

Last iterate convergence of SGD for Least-Squares in the Interpolation regime

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

Universal and adaptive methods for robust stochastic optimization

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

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

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

Large Steps in Inverse Rendering of Geometry

Last iterate convergence of SGD for Least-Squares in the Interpolation regime

Regret Minimization in Stochastic Non-Convex Learning via a Proximal-Gradient Approach

STORM+: Fully Adaptive SGD with Momentum for Nonconvex Optimization

A Newton Frank-Wolfe method for constrained self-concordant minimization

UNDERGRAD: A Universal Black-Box Optimization Method with Almost Dimension-Free Convergence Rate Guarantees