Search Results for author:
Found 13 papers, 4 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.
Near-optimal Closed-loop Method via Lyapunov Damping for Convex Optimization
We introduce an autonomous system with closed-loop damping for first-order convex optimization.
PAC-Bayesian Learning of Optimization Algorithms
We apply the PAC-Bayes theory to the setting of learning-to-optimize.
Fixed-Point Automatic Differentiation of Forward--Backward Splitting Algorithms for Partly Smooth Functions
A large class of non-smooth practical optimization problems can be written as minimization of a sum of smooth and partly smooth functions.
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.
Self-supervised Sparse to Dense Motion Segmentation
We evaluate our method on the well-known motion segmentation datasets FBMS59 and DAVIS16.
Bregman Proximal Framework for Deep Linear Neural Networks
This initiated the development of the Bregman proximal gradient (BPG) algorithm and an inertial variant (momentum based) CoCaIn BPG, which however rely on problem dependent Bregman distances.
Beyond Alternating Updates for Matrix Factorization with Inertial Bregman Proximal Gradient Algorithms
Matrix Factorization is a popular non-convex optimization problem, for which alternating minimization schemes are mostly used.
Convex-Concave Backtracking for Inertial Bregman Proximal Gradient Algorithms in Non-Convex Optimization
Backtracking line-search is an old yet powerful strategy for finding a better step sizes to be used in proximal gradient algorithms.
Model Function Based Conditional Gradient Method with Armijo-like Line Search
The Conditional Gradient Method is generalized to a class of non-smooth non-convex optimization problems with many applications in machine learning.
Lifting Layers: Analysis and Applications
A lifting layer increases the dimensionality of the input, naturally yields a linear spline when combined with a fully connected layer, and therefore closes the gap between low and high dimensional approximation problems.
iPiano: Inertial Proximal Algorithm for Non-Convex Optimization
A rigorous analysis of the algorithm for the proposed class of problems yields global convergence of the function values and the arguments.
An Iterated L1 Algorithm for Non-smooth Non-convex Optimization in Computer Vision
Here we extend the problem class to linearly constrained optimization of a Lipschitz continuous function, which is the sum of a convex function and a function being concave and increasing on the non-negative orthant (possibly non-convex and nonconcave on the whole space).
