Publications associées à Nondyadic and nonlinear multiresolution image approximations

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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

Nondyadic and nonlinear multiresolution image approximations

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Eigendecomposition-Free Training of Deep Networks for Linear Least-Square Problems

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

Frequency-Domain Diffusion Adaptation Over Networks

Superluminal Motion-Assisted Four-Dimensional Light-in-Flight Imaging

Shortest-support multi-spline bases for generalized sampling

Convergence of adaptive algorithms for constrained weakly convex optimization

Functional Inverse Problems on Spheres: Theory, Algorithms and Applications

Continuous-Domain Signal Reconstruction Using L-p-Norm Regularization

Convergences of Regularized Algorithms and Stochastic Gradient Methods with Random Projections

Coordinate Difference Matrices

Convergences of Regularized Algorithms and Stochastic Gradient Methods with Random Projections

Frequency-Domain Diffusion Adaptation Over Networks

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

Superluminal Motion-Assisted Four-Dimensional Light-in-Flight Imaging

Shortest-support multi-spline bases for generalized sampling

Coordinate Difference Matrices

Convergence of adaptive algorithms for constrained weakly convex optimization

Functional Inverse Problems on Spheres: Theory, Algorithms and Applications

Continuous-Domain Signal Reconstruction Using L-p-Norm Regularization

Eigendecomposition-Free Training of Deep Networks for Linear Least-Square Problems