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Found 15 papers, 2 papers with code
Learning-to-Optimize with PAC-Bayesian Guarantees: Theoretical Considerations and Practical Implementation
We use the PAC-Bayesian theory for the setting of learning-to-optimize.
Recovery Guarantees of Unsupervised Neural Networks for Inverse Problems trained with Gradient Descent
Advanced machine learning methods, and more prominently neural networks, have become standard to solve inverse problems over the last years.
Convergence and Recovery Guarantees of Unsupervised Neural Networks for Inverse Problems
Neural networks have become a prominent approach to solve inverse problems in recent years.
Convergence Guarantees of Overparametrized Wide Deep Inverse Prior
Neural networks have become a prominent approach to solve inverse problems in recent years.
Provable Phase Retrieval with Mirror Descent
In this paper, we consider the problem of phase retrieval, which consists of recovering an $n$-dimensional real vector from the magnitude of its $m$ linear measurements.
A Stochastic Bregman Primal-Dual Splitting Algorithm for Composite Optimization
Under slightly stricter assumptions, we show almost sure weak convergence of the pointwise iterates to a saddle point.
Global Convergence of Model Function Based Bregman Proximal Minimization Algorithms
We fix this issue by proposing the MAP property, which generalizes the $L$-smad property and is also valid for a large class of nonconvex nonsmooth composite problems.
Inexact and Stochastic Generalized Conditional Gradient with Augmented Lagrangian and Proximal Step
In this paper we propose and analyze inexact and stochastic versions of the CGALP algorithm developed in the authors' previous paper, which we denote ICGALP, that allows for errors in the computation of several important quantities.
Wasserstein Control of Mirror Langevin Monte Carlo
In this paper, we consider Langevin diffusions on a Hessian-type manifold and study a discretization that is closely related to the mirror-descent scheme.
Learning CHARME models with neural networks
In this paper, we consider a model called CHARME (Conditional Heteroscedastic Autoregressive Mixture of Experts), a class of generalized mixture of nonlinear nonparametric AR-ARCH time series.
Sensitivity Analysis for Mirror-Stratifiable Convex Functions
This pairing is crucial to track the strata that are identifiable by solutions of parametrized optimization problems or by iterates of optimization algorithms.
Sparse Support Recovery with Non-smooth Loss Functions
More precisely, we focus in detail on the cases of $\ell_1$ and $\ell_\infty$ losses, and contrast them with the usual $\ell_2$ loss. While these losses are routinely used to account for either sparse ($\ell_1$ loss) or uniform ($\ell_\infty$ loss) noise models, a theoretical analysis of their performance is still lacking.
A Multi-step Inertial Forward-Backward Splitting Method for Non-convex Optimization
In this paper, we propose a multi-step inertial Forward--Backward splitting algorithm for minimizing the sum of two non-necessarily convex functions, one of which is proper lower semi-continuous while the other is differentiable with a Lipschitz continuous gradient.
Local Linear Convergence of Forward--Backward under Partial Smoothness
In this paper, we consider the Forward--Backward proximal splitting algorithm to minimize the sum of two proper closed convex functions, one of which having a Lipschitz continuous gradient and the other being partly smooth relatively to an active manifold $\mathcal{M}$.
Astronomical Image Denoising Using Dictionary Learning
Astronomical images suffer a constant presence of multiple defects that are consequences of the intrinsic properties of the acquisition equipments, and atmospheric conditions.
