no code implementations • 7 Jul 2023 • Chris Kolb, Christian L. Müller, Bernd Bischl, David Rügamer

Additionally, our theory establishes results of independent interest regarding matching local minima for arbitrary, potentially unregularized, objectives.

no code implementations • 25 May 2022 • David Rügamer, Andreas Bender, Simon Wiegrebe, Daniel Racek, Bernd Bischl, Christian L. Müller, Clemens Stachl

Here, we propose Factorized Structured Regression (FaStR) for scalable varying coefficient models.

no code implementations • 16 Dec 2021 • Dominik Thalmeier, Gregor Miller, Elida Schneltzer, Anja Hurt, Martin Hrabě de Angelis, Lore Becker, Christian L. Müller, Holger Maier

In a high-throughput mouse phenotyping environment, both methods perform well as part of an automated end-to-end screening pipeline to detect candidate genes for hearing involvement.

2 code implementations • 21 Jun 2021 • Elisabeth Ailer, Christian L. Müller, Niki Kilbertus

Many scientific datasets are compositional in nature.

2 code implementations • 6 Apr 2021 • David Rügamer, Chris Kolb, Cornelius Fritz, Florian Pfisterer, Philipp Kopper, Bernd Bischl, Ruolin Shen, Christina Bukas, Lisa Barros de Andrade e Sousa, Dominik Thalmeier, Philipp Baumann, Lucas Kook, Nadja Klein, Christian L. Müller

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 additive regression models and deep networks.

no code implementations • 15 Jan 2021 • Suryanarayana Maddu, Dominik Sturm, Bevan L. Cheeseman, Christian L. Müller, Ivo F. Sbalzarini

Often, this requires high-resolution or adaptive discretization grids to capture relevant spatio-temporal features in the PDE solution, e. g., in applications like turbulence, combustion, and shock propagation.

no code implementations • 11 Dec 2020 • Suryanarayana Maddu, Bevan L. Cheeseman, Christian L. Müller, Ivo F. Sbalzarini

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 symmetries when learning or inferring differential-equation models from measurement data.

1 code implementation • 2 Nov 2020 • Léo Simpson, Patrick L. Combettes, Christian L. Müller

The underlying statistical forward model is assumed to be of the following form: \[ y = X \beta + \sigma \epsilon \qquad \textrm{subject to} \qquad C\beta=0 \] Here, $X \in \mathbb{R}^{n\times d}$is a given design matrix and the vector $y \in \mathbb{R}^{n}$ is a continuous or binary response vector.

2 code implementations • 24 Jun 2020 • Grace Yoon, Christian L. Müller, Irina Gaynanova

Latent Gaussian copula models provide a powerful means to perform multi-view data integration since these models can seamlessly express dependencies between mixed variable types (binary, continuous, zero-inflated) via latent Gaussian correlations.

Computation Methodology

1 code implementation • 17 Jul 2019 • Suryanarayana Maddu, Bevan L. Cheeseman, Ivo F. Sbalzarini, Christian L. Müller

We show that in particular the combination of stability selection with the iterative hard-thresholding algorithm from compressed sensing provides a fast, parameter-free, and robust computational framework for PDE inference that outperforms previous algorithmic approaches with respect to recovery accuracy, amount of data required, and robustness to noise.

1 code implementation • 4 Mar 2019 • Patrick L. Combettes, Christian L. Müller

This model describes the response as a linear combination of log-ratios of the original compositions and has been extended to the high-dimensional setting via regularization.

Statistics Theory Statistics Theory

1 code implementation • 16 May 2018 • Patrick L. Combettes, Christian L. Müller

We introduce an optimization model for maximum likelihood-type estimation (M-estimation) that generalizes a large class of existing statistical models, including Huber's concomitant M-estimator, Owen's Huber/Berhu concomitant estimator, the scaled lasso, support vector machine regression, and penalized estimation with structured sparsity.

Statistics Theory Statistics Theory

4 code implementations • 23 May 2016 • Christian L. Müller, Richard Bonneau, Zachary Kurtz

Selecting regularization parameters in penalized high-dimensional graphical models in a principled, data-driven, and computationally efficient manner continues to be one of the key challenges in high-dimensional statistics.

Methodology Molecular Networks Applications Computation

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