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1 code implementation • 11 Mar 2022 • Nikolaj Thams, Rikke Søndergaard, Sebastian Weichwald, Jonas Peters

To do so, we develop nuisance IV, which can be of interest even in the i. i. d.

1 code implementation • 12 Feb 2022 • Sebastian Weichwald, Søren Wengel Mogensen, Tabitha Edith Lee, Dominik Baumann, Oliver Kroemer, Isabelle Guyon, Sebastian Trimpe, Jonas Peters, Niklas Pfister

Questions in causality, control, and reinforcement learning go beyond the classical machine learning task of prediction under i. i. d.

no code implementations • 29 Mar 2021 • Eigil F. Rischel, Sebastian Weichwald

Interventional causal models describe several joint distributions over some variables used to describe a system, one for each intervention setting.

1 code implementation • NeurIPS 2021 • Alexander G. Reisach, Christof Seiler, Sebastian Weichwald

Here, we show that marginal variance tends to increase along the causal order for generically sampled additive noise models.

1 code implementation • 21 Feb 2020 • Sebastian Weichwald, Martin E Jakobsen, Phillip B Mogensen, Lasse Petersen, Nikolaj Thams, Gherardo Varando

In this article, we describe the algorithms for causal structure learning from time series data that won the Causality 4 Climate competition at the Conference on Neural Information Processing Systems 2019 (NeurIPS).

no code implementations • 14 Feb 2020 • Sebastian Weichwald, Jonas Peters

Robustness (or invariance) is a fundamental principle underlying causal methodology.

3 code implementations • 4 Jun 2018 • Niklas Pfister, Sebastian Weichwald, Peter Bühlmann, Bernhard Schölkopf

We introduce coroICA, confounding-robust independent component analysis, a novel ICA algorithm which decomposes linearly mixed multivariate observations into independent components that are corrupted (and rendered dependent) by hidden group-wise stationary confounding.

no code implementations • 4 Jul 2017 • Paul K. Rubenstein, Sebastian Weichwald, Stephan Bongers, Joris M. Mooij, Dominik Janzing, Moritz Grosse-Wentrup, Bernhard Schölkopf

Complex systems can be modelled at various levels of detail.

no code implementations • 23 May 2016 • Sebastian Weichwald, Tatiana Fomina, Bernhard Schölkopf, Moritz Grosse-Wentrup

While the channel capacity reflects a theoretical upper bound on the achievable information transmission rate in the limit of infinitely many bits, it does not characterise the information transfer of a given encoding routine with finitely many bits.

1 code implementation • 2 May 2016 • Sebastian Weichwald, Arthur Gretton, Bernhard Schölkopf, Moritz Grosse-Wentrup

Causal inference concerns the identification of cause-effect relationships between variables.

1 code implementation • 10 Mar 2016 • James Townsend, Niklas Koep, Sebastian Weichwald

Optimization on manifolds is a class of methods for optimization of an objective function, subject to constraints which are smooth, in the sense that the set of points which satisfy the constraints admits the structure of a differentiable manifold.

no code implementations • 15 Dec 2015 • Sebastian Weichwald, Bernhard Schölkopf, Tonio Ball, Moritz Grosse-Wentrup

Pattern recognition in neuroimaging distinguishes between two types of models: encoding- and decoding models.

no code implementations • 14 Dec 2015 • Sebastian Weichwald, Timm Meyer, Bernhard Schölkopf, Tonio Ball, Moritz Grosse-Wentrup

While invasively recorded brain activity is known to provide detailed information on motor commands, it is an open question at what level of detail information about positions of body parts can be decoded from non-invasively acquired signals.

1 code implementation • 3 Dec 2015 • Sebastian Weichwald, Moritz Grosse-Wentrup, Arthur Gretton

Causal inference concerns the identification of cause-effect relationships between variables, e. g. establishing whether a stimulus affects activity in a certain brain region.

no code implementations • 15 Nov 2015 • Sebastian Weichwald, Timm Meyer, Ozan Özdenizci, Bernhard Schölkopf, Tonio Ball, Moritz Grosse-Wentrup

Causal terminology is often introduced in the interpretation of encoding and decoding models trained on neuroimaging data.

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