2k citations · 2k across the 10 of their papers we have counts for
10 papers
Smoothing the Edges: Smooth Optimization for Sparse Regularization using Hadamard Overparametrization
Chris Kolb, Christian L. Müller, Bernd Bischl +1
We present a framework for smooth optimization of explicitly regularized objectives for (structured) sparsity. These non-smooth and possibly non-convex problems typically rely on s…
GGLasso -- a Python package for General Graphical Lasso computation
Fabian Schaipp, Christian L. Müller, Oleg Vlasovets
We introduce GGLasso, a Python package for solving General Graphical Lasso problems. The Graphical Lasso scheme, introduced by (Friedman 2007) (see also (Yuan 2007; Banerjee 2008))…
Inverse-Dirichlet Weighting Enables Reliable Training of Physics Informed Neural Networks
Suryanarayana Maddu, Dominik Sturm, Christian L. Müller +1
We characterize and remedy a failure mode that may arise from multi-scale dynamics with scale imbalances during training of deep neural networks, such as Physics Informed Neural Ne…
deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression
David Rügamer, Chris Kolb, Cornelius Fritz +11
In this paper we describe the implementation of semi-structured deep distributional regression, a flexible framework to learn conditional distributions based on the combination of…
STENCIL-NET: Data-driven solution-adaptive discretization of partial differential equations
Suryanarayana Maddu, Dominik Sturm, Bevan L. Cheeseman +2
Numerical methods for approximately solving partial differential equations (PDE) are at the core of scientific computing. Often, this requires high-resolution or adaptive discretiz…
Learning physically consistent mathematical models from data using group sparsity
Suryanarayana Maddu, Bevan L. Cheeseman, Christian L. Müller +1
We propose a statistical learning framework based on group-sparse regression that can be used to 1) enforce conservation laws, 2) ensure model equivalence, and 3) guarantee symmetr…
Stability selection enables robust learning of partial differential equations from limited noisy data
Suryanarayana Maddu, Bevan L. Cheeseman, Ivo F. Sbalzarini +1
We present a statistical learning framework for robust identification of partial differential equations from noisy spatiotemporal data. Extending previous sparse regression approac…
Topology Adaptive Graph Estimation in High Dimensions
Johannes Lederer, Christian Müller
We introduce Graphical TREX (GTREX), a novel method for graph estimation in high-dimensional Gaussian graphical models. By conducting neighborhood selection with TREX, GTREX avoids…
Sparse and compositionally robust inference of microbial ecological networks
Zachary D. Kurtz, Christian L. Mueller, Emily R. Miraldi +3
16S-ribosomal sequencing and other metagonomic techniques provide snapshots of microbial communities, revealing phylogeny and the abundances of microbial populations across diverse…
Global parameter identification of stochastic reaction networks from single trajectories
Christian L. Muller, Rajesh Ramaswamy, Ivo F. Sbalzarini
We consider the problem of inferring the unknown parameters of a stochastic biochemical network model from a single measured time-course of the concentration of some of the involve…