Sebastian Weichwald.

News / Publications / Misc / Appeal for accessible research / Olds

I am interested in causal inference and its potential to provide novel insights in neuroimaging. We have provided a comprehensive set of causal interpretation rules for encoding and decoding models in neuroimaging studies (see this publication and this explainer video (5 min)). Currently I am working with Arthur Gretton and Moritz Grosse-Wentrup on causal effect recovery from linear mixtures (see MERLiN and this manuscript).

Since 2015/11 I am a PhD student at the Max Planck Institute for Intelligent Systems, supervised by Bernhard Schölkopf and Moritz Grosse-Wentrup. Throughout my studies I have been working there as research assistant. I obtained my MSc in Computational Statistics and Machine Learning from University College London, funded by the German National Academic Foundation (Studienstiftung) and the German Academic Exchange Service (DAAD), and my BSc in Mathematics from the University of Tübingen.

I am happy to receive criticism, comments or helpful suggestions via email: .


Jun 2016.
Attending OHBM 2016 – exciting! Thanks to Russell Poldrack, Martin Lindquist, and Christoph Herrmann for a great symposium! (The slides for my talk can be found here.)
Mar 2016.
Our symposium "What Neuroimaging Can Tell Us? From Correlation to Causation and Cognitive Ontologies" has been accepted for the OHBM 2016 Annual Meeting. I feel honoured to be part of this symposium with Russell Poldrack, Martin Lindquist, and Christoph Herrmann.
Mar 2016.
We have released an early version of Pymanopt: A Python Toolbox for Manifold Optimization using Automatic Differentiation. This example demonstrates how to infer the parameters of a Mixture of Gaussian (MoG) model using manifold optimisation instead of expectation maximisation (EM).
Dec 2015.
New manuscript and code is out: MERLiN: Mixture Effect Recovery in Linear Networks.


Peer-reviewed articles.


MERLiN: Mixture Effect Recovery in Linear Networks

S Weichwald, M Grosse-Wentrup, A Gretton
IEEE Journal of Selected Topics in Signal Processing, 10(7):12541266, 2016

MERLiN is a causal inference algorithm that can recover from an observed linear mixture a causal variable that is an effect of another given variable. MERLiN implements a novel idea on how to (re-)construct causal variables and is robust against hidden confounding.

arXiv / code / doi / pdf

Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation

J Townsend, N Koep, S Weichwald
Journal of Machine Learning Research, 17(137):15, 2016

Pymanopt lowers the barriers to users wishing to use state of the art manifold optimization techniques, by using automated differentiation for calculating derivative information, saving users time and saving them from potential calculation and implementation errors.
(Example: manifold optimisation for inferring parameters of a MoG model.)

arXiv / code / documentation / jmlr / pdf

Causal interpretation rules for encoding and decoding models in neuroimaging

S Weichwald, T Meyer, O Özdenizci, B Schölkopf, T Ball, M Grosse-Wentrup
NeuroImage, 110:4859, 2015

We provide a set of rules which causal statements are warranted and which ones are not supported by empirical evidence. Especially, only encoding models in the stimulus-based setting support unambiguous causal interpretations. By combining encoding and decoding models, however, we obtain insights into causal relations beyond those that are implied by each individual model type.

arXiv / doi / explainer video (5 min)
pdf / slides / tutorial video (35 min)

Peer-reviewed conference papers.


Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data

S Weichwald, A Gretton, B Schölkopf, M Grosse-Wentrup
International Workshop on Pattern Recognition in Neuroimaging (PRNI), 2016

This paper proposes an extension of the MERLiN algorithm to identify non-linear cause-effect relationships between linearly mixed neuroimaging data.

arXiv / code / doi / pdf / slides

Causal and anti-causal learning in pattern recognition for neuroimaging

S Weichwald, B Schölkopf, T Ball, M Grosse-Wentrup
International Workshop on Pattern Recognition in Neuroimaging (PRNI), 2014

In this paper, we argue that it is not sufficient to distinguish between encoding- and decoding models: The interpretation of such models depends on whether they are employed in a stimulus- or response-based setting.

arXiv / doi / pdf

Decoding index finger position from EEG using random forests

S Weichwald, T Meyer, B Schölkopf, T Ball, M Grosse-Wentrup
International Workshop on Cognitive Information Processing (CIP), 2014
(best student paper award)

In this work it is shown that index finger positions can be differentiated from non-invasive EEG recordings in healthy human subjects. Among the different spectral features investigated, high β-power (20–30 Hz) over contralateral sensorimotor cortex carried most information about finger position.

arXiv / doi / pdf



Optimal Coding in Biological and Artificial Neural Networks

S Weichwald, T Fomina, B Schölkopf, M Grosse-Wentrup

This work suggests that the layer-wise similarity of feature representations in biological and artificial neural networks is a result of optimal coding that enables robust transmission of object information over noisy channels. Our work further provides a plausible explanation why optimal codes can be learned in unsupervised settings.

arXiv / pdf

Appeal for open access, preprints and accepted manuscripts.

In my opinion research results should be accessible to everyone. If not publishing open access, authors should consider posting preprints or accepted manuscripts on arXiv or their personal website. The SHERPA/RoMEO database makes it easy to check a journal's/publisher's policy and decide on posting a preprint or author accepted manuscript.



Causal Effect Recovery from Linear Mixtures (CERLiM)

2015. Consider a randomised instrumental variable S and another pre-specified variable X. Causal effect recovery from linear mixtures aims to identify Y from an observed linearly mixed signal such that S→X→Y. We present five related algorithms for recovery of causal effects even in the presence of hidden confounders. Evaluation on both synthetic and EEG data indicates usefulness and applicability of our method.


What is Cantor's continuum problem?

2013. This seminar paper reviews Kurt Gödel's article »What is Cantor's continuum problem?«. As this paper aims to be almost self-contained, short recaps, rough explanations and selective examples are provided where appropriate.


Langton's Ant (MATLAB-Simulation)

2011. A few small scripts which allow to simulate the ant's behaviour within different two-dimensional grids with different kinds of borders. The ant is represented by a little red triangle which allows to indicate the current direction. One can follow the ant move by move, step by step or in fast-forward mode.



Jan 2016.
Moritz's OHBM 2015 educational talk Causal Interpretation Rules for Encoding and Decoding Models in Neuroimaging is online.
Aug 2015.
Completed my master's at UCL with a thesis on causal effect recovery from linear mixtures. It's time for holidays in the United States!
Besides an exciting road trip I am also looking forward to interesting intermezzi: I will present our recent work at the Poldrack Lab (Stanford University, September), the LIINC group (Columbia University, October), and will visit Martin Lindquist (Johns Hopkins University, October).
Jun 2015.
A new explainer video (5 min) describing our work is online.
I will present our recent work at this year's UAI Workshop "Advances in Causal Inference" (Amsterdam, July).
May 2015.
Moritz will present our recent work at this year's PRNI workshop (Stanford University, June).
I have been invited for talks at the FMRIB Analysis Group (University of Oxford, July) and the LIINC group (Columbia University, October). Furthermore, I will be visiting Martin Lindquist (Johns Hopkins University, October).
– Looking forward to meeting inspiring people and having interesting discussions!
Nov 2014.
A golden oldie worth a reread: Data Set Selection by Doudou LaLoudouana and Mambobo Bonouliqui Tarare.
Aug 2014.
Let's have a drink, let's have a Kernel!
Jun 2014.
A new short media report on the BCI group's work is online.
English version: video / article; German version video / article.
Feb 2014.
An interesting article: Scientific method: Statistical errors : Nature News & Comment.
Jan 2014.
Videos and slides of MLSS 2013 Tübingen can now be downloaded.
Oct 2013.
An interesting article: Unreliable research: Trouble at the lab | The Economist.
Imprint & Credits