These are my academic publications, see
researchgate and
google scholar.
Here, I'll leave some informal words about their topics.
Journal Papers
A complete overview of the finite element modeling steps for the special case of the stationary heat equation in engineering.
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M. Fuchs, R. Hornung, A.-L. Boulesteix, R. De Bin: On the asymptotic behavior
of the variance estimator of a U-statistic. Journal of Statistical Planning and
Inference, 2020.
https://doi.org/10.1016/j.jspi.2020.03.003
We show that that the error rate of a supervised learning algorithm can be estimated without bias as
long as the test set is at least twice as large as the learning set.
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M. Fuchs, R. Neumayr: Agent-Based Semiology for Simulation and Prediction of
Contemporary Spatial Occupation Patterns. In: Gengnagel C., Baverel O., Burry J., Ramsgaard
Thomsen M., Weinzierl S. (eds) Impact: Design With All Senses. Proceedings of the Design
Modelling Symposium, Berlin 2019,
https://doi.org/10.1007/978-3-030-29829-6_50, pages 648 - 661
We show a way to make the results of pedestrian simulation availabe to the designer. It relies on a
spatial regression of the simulated density on distance fields and other architectural features.
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V.Booshan, M. Fuchs, S. Bhooshan: 3D-Printing, Topology Optimization and
Statistical Learning: A Case Study. Proceedings of the Symposium on Simulation for Architecture
and Urban Design (2017),
https://dl.acm.org/doi/10.5555/3289787.3289799,
My first architectural paper. We described outputs of a finite element software in terms of
geometrical features, by a linear regression. This was fun and interesting, but the most important
part was finding out that the surface normal's z component was an important explanatory variable in
that linear regression. We found out about that by investigating the Airy approach to structural
mechanics more closely.
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A.-L. Boulesteix, R. De Bin, X. Jiang, M. Fuchs: IPF-LASSO: Integrative
-Penalized Regression with Penalty Factors for Prediction Based on Multi-Omics Data,
Computational and Mathematical Methods in Medicine, 2017.
https://doi.org/10.1155/2017/7691937
This paper is mostly based on the preprint https://epub.ub.uni-muenchen.de/26551/
(Technical Report 187),
Department of Statistics, Ludwig Maximilian University of Munich.
Here, we took a look at the problem of integrating different group of covariables, the prototypical
situation being when mRNA and miRNA are to be integrated. We pursue a fairly immediate line of
thought: to apply different lasso penalizations to each of them. The details, however, are quite
nitty-gritty. We show how penalized regression generalises naturally to multiple data sources, by
penalising each modality differently.
Yet another topic: theoretical statistics. This is my second favorite paper on this side. We
identified and computed certain covariances between evaluations of error estimators. Furthermore, we
identified U-statistics to show how these covariances can be estimated.
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M. Fuchs: Equivariant K-homology of Bianchi groups in the case of non-trivial
class group. Münster Journal of Mathematics 7 (2014), 589–607
http://doi.org/10.17879/58269758846
Back to pure Mathematics. This paper is in spirit not too far away from that of my PhD thesis. I
think this paper is the one I am most proud of.
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M. Fuchs, T.Beissbarth, E.Wingender, K.Jung: Connecting high-dimensional mRNA
and miRNA expression data for binary medical classification problems, Computer Methods and
Programs in Biomedicine (2013), http://dx.doi.org/10.1016/j.cmpb.2013.05.013
Yet another topic: a general overview of data combination strategies. In retrospect, there are
better
ones out there, though, than the ones we looked at.
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V.Halacheva, M. Fuchs, J.Dönitz, T.Reupke, B.Püschel, C.Viebahn: Complex Planar
Cell Movement and Oriented Cell Division Build the Early Primitive Streak in the Mammalian
Embryo, Developmental Dynamics 240, 1905-1916 (2011),
https://dx.doi.org/10.1002/dvdy.22687
Once again, a completely different topic: biology, and in particular developmental biology. The
primitive streak is the first morphogenic feature in the nascent mammalian embryo. We applied
circular statistics to show that cell divisions prefer certain directions.
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M.Ante, E.Wingender, M. Fuchs: Integration of gene expression data with prior
knowledge for network analysis and validation, BMC Research Notes 4, 520 (2011),
https://doi.org/10.1186/1756-0500-4-520
My first non-mathematical paper: Some work on biological databases, of a rather
algebraic/non-quantitative flavor. Biological databasesnothing I really worked on before or
after anytime again ....
The title says it all: we compute the group homology of a certain class of groups acting on
hyperbolic three-space. Was (mostly) fun. I learned a lot about homological algebra, and about
fundamental sets of actions.
Thesis
PhD thesis: M. Fuchs, "Cohomologie cyclique des produits croises associes aux
groupes de Lie". Written at the Institut de Mathematiques de Luminy under the direction of Michael
Puschnigg at the Universite de la Mediterranee Aix-Marseile,
https://arxiv.org/abs/math/0612023
I give a completely new proof of the fact that the group ring of torsion-free discrete co-compact
subgroups of SL(n, C) satisfies the Kadison-Kaplansky conjecture: it is free of non-trivial
idempotents. Unfortunately, I never came back to that topic later.
Technical Reports
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M. Fuchs, X. Jiang, A.-L. Boulesteix, 2016. The computationally optimal test
set size in simulation studies on supervised learning.
https://epub.ub.uni-muenchen.de/26870/
(Technical Report 189), Department of Statistics, Ludwig Maximilian University of Munich
A simulation study on supervised learning is when the following process is carried out repeatedly,
with independent repetitions: One draws a learning sample from a distribution. The size of the
learning sample, i.e., the number of observations it is comprised of, is to be kept the same
throughout. Each observation consists of predictors or (covariables), and an outcome or value of
response variable, for instance, a binary prediction target. Furthermore, one evaluates the
performance of the predictive model thus achieved, by evaluating it on a test set. Averageing out
across all independent iterations is guaranteed to converge towards the true expectated value of the
error estimator - the true error. It might seem a little unintuitive that the latter statement holds
true regardless of the size of the test set. The reason is that the mentioned expected value is
unaffected by the size of the test set. Therefore, it is just a matter of computational convenience
to choose it carefully. It is the goal of this paper to perform that choice in an educated way, as a
function of the machine computation execution speed.
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M. Fuchs, R. Hornung, R. De Bin, A.-L. Boulesteix, 2013. A U-statistic
estimator for the variance of resampling-based error estimators.
http://epub.ub.uni-muenchen.de/17654/
(Technical Report 147), Department of Statistics, Ludwig Maximilian University of Munich
We show a bunch of things about the errror estimator of a machine learning algorithm, on a real data
sample of fxed size (typically denoted by n in statistics). Among several statements, we show that
the studentized error estimator is asymptotically normally distributed. This is a new contribution.
