Publications associées à A Unifying Representer Theorem for Inverse Problems and Machine Learning

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Convergence of adaptive algorithms for constrained weakly convex optimization

Volkan Cevher, Ahmet Alacaoglu, Yurii Malitskyi

We analyze the adaptive first order algorithm AMSGrad, for solving a constrained stochastic optimization problem with a weakly convex objective. We prove the

\tilde O(t^{-1/2})

rate of convergence for the squared norm of the gradient of Moreau envelope, ...

2021

A Unifying Representer Theorem for Inverse Problems and Machine Learning

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

Comparison of non-parametric T2 relaxometry methods for myelin water quantification

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

Geodesic Distance on Optimally Regularized Functional Connectomes Uncovers Individual Fingerprints

Convergence of adaptive algorithms for constrained weakly convex optimization

Wasserstein Adversarial Regularization for learning with label noise

Joint Graph-based Depth Refinement and Normal Estimation

Good Vibrations and Unknown Excitations

Functional Penalised Basis Pursuit on Spheres

Scalable Convex Optimization Methods for Semidefinite Programming

Comparison of non-parametric T2 relaxometry methods for myelin water quantification

Good Vibrations and Unknown Excitations

Scalable Convex Optimization Methods for Semidefinite Programming

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

Geodesic Distance on Optimally Regularized Functional Connectomes Uncovers Individual Fingerprints

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

Wasserstein Adversarial Regularization for learning with label noise

Functional Penalised Basis Pursuit on Spheres

Convergence of adaptive algorithms for constrained weakly convex optimization

Joint Graph-based Depth Refinement and Normal Estimation