Publications associées à Regularized Bundle Methods for Convex and Non-Convex Risks

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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

Regularized Bundle Methods for Convex and Non-Convex Risks

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

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

Integrating advanced discrete choice models in mixed integer linear optimization

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

Addressing fairness in classification with a model-agnostic multi-objective algorithm

Mathematical Foundations of Robust and Distributionally Robust Optimization

Large Steps in Inverse Rendering of Geometry

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

Adaptive Gradient Descent without Descent

Convergence without Convexity: Sampling, Optimization, and Games

Large Steps in Inverse Rendering of Geometry

Integrating advanced discrete choice models in mixed integer linear optimization

Convergence without Convexity: Sampling, Optimization, and Games

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

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

Mathematical Foundations of Robust and Distributionally Robust Optimization

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

Adaptive Gradient Descent without Descent

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

Addressing fairness in classification with a model-agnostic multi-objective algorithm