5 citations · 10 across the 5 of their papers we have counts for
9 papers
Differentiating the Value Function by using Convex Duality
Sheheryar Mehmood, Peter Ochs
We consider the differentiation of the value function for parametric optimization problems. Such problems are ubiquitous in Machine Learning applications such as structured support…
Global Convergence of Model Function Based Bregman Proximal Minimization Algorithms
Mahesh Chandra Mukkamala, Jalal Fadili, Peter Ochs
Lipschitz continuity of the gradient mapping of a continuously differentiable function plays a crucial role in designing various optimization algorithms. However, many functions ar…
Self-supervised Sparse to Dense Motion Segmentation
Amirhossein Kardoost, Kalun Ho, Peter Ochs +1
Observable motion in videos can give rise to the definition of objects moving with respect to the scene. The task of segmenting such moving objects is referred to as motion segment…
Automatic Differentiation of Some First-Order Methods in Parametric Optimization
Sheheryar Mehmood, Peter Ochs
We aim at computing the derivative of the solution to a parametric optimization problem with respect to the involved parameters. For a class broader than that of strongly convex fu…
Bregman Proximal Framework for Deep Linear Neural Networks
Mahesh Chandra Mukkamala, Felix Westerkamp, Emanuel Laude +2
A typical assumption for the analysis of first order optimization methods is the Lipschitz continuity of the gradient of the objective function. However, for many practical applica…
Bregman Proximal Mappings and Bregman-Moreau Envelopes under Relative Prox-Regularity
Emanuel Laude, Peter Ochs, Daniel Cremers
We systematically study the local single-valuedness of the Bregman proximal mapping and local smoothness of the Bregman--Moreau envelope of a nonconvex function under relative prox…