Publications associées à Continuity Properties of Law-Invariant (Quasi-)Convex Risk Functions on L∞

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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

Continuity Properties of Law-Invariant (Quasi-)Convex Risk Functions on L∞

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Chattez avec Graph Search

Stability of Image-Reconstruction Algorithms

Gradient-based optimisation of the conditional-value-at-risk using the multi-level Monte Carlo method

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

Convex optimization in sums of Banach spaces

Adaptive Gradient Descent without Descent

A Multi-Agent Primal-Dual Strategy for Composite Optimization over Distributed Features

On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems

Stochastic Frank-Wolfe for Composite Convex Minimization

Discrete-time regional pole-placement using convex approximations: Theory and application to a boost converter

Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation

Adaptive Gradient Descent without Descent

Discrete-time regional pole-placement using convex approximations: Theory and application to a boost converter

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

Stability of Image-Reconstruction Algorithms

Gradient-based optimisation of the conditional-value-at-risk using the multi-level Monte Carlo method

Stochastic Frank-Wolfe for Composite Convex Minimization

Convex optimization in sums of Banach spaces

Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation

On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems

A Multi-Agent Primal-Dual Strategy for Composite Optimization over Distributed Features