1 citations · 1 across the 3 of their papers we have counts for
6 papers
Why the noise model matters: A performance gap in learned regularization
Sebastian Banert, Christoph Brauer, Dirk Lorenz +1
This article addresses the challenge of learning effective regularizers for linear inverse problems. We analyze and compare several types of learned variational regularization agai…
Learning Variational Models with Unrolling and Bilevel Optimization
Christoph Brauer, Niklas Breustedt, Timo de Wolff +1
In this paper we consider the problem of learning variational models in the context of supervised learning via risk minimization. Our goal is to provide a deeper understanding of t…
Primal-dual residual networks
Christoph Brauer, Dirk Lorenz
In this work, we propose a deep neural network architecture motivated by primal-dual splitting methods from convex optimization. We show theoretically that there exists a close rel…
A Sinkhorn-Newton method for entropic optimal transport
Christoph Brauer, Christian Clason, Dirk Lorenz +1
We consider the entropic regularization of discretized optimal transport and propose to solve its optimality conditions via a logarithmic Newton iteration. We show a quadratic conv…
A Primal-Dual Homotopy Algorithm for -Minimization with -Constraints
Christoph Brauer, Dirk A. Lorenz, Andreas M. Tillmann
In this paper we propose a primal-dual homotopy method for -minimization problems with infinity norm constraints in the context of sparse reconstruction. The natural homoto…
Rank-optimal weighting or "How to be best in the OECD Better Life Index?"
Jan Lorenz, Christoph Brauer, Dirk A. Lorenz
We present a method of rank-optimal weighting which can be used to explore the best possible position of a subject in a ranking based on a composite indicator by means of a mathema…