1 code implementation • 16 Oct 2023 • Paweł Czyż, Frederic Grabowski, Julia E. Vogt, Niko Beerenwinkel, Alexander Marx

Mutual information quantifies the dependence between two random variables and remains invariant under diffeomorphisms.

no code implementations • 11 Oct 2023 • Mirco Mutti, Riccardo De Santi, Marcello Restelli, Alexander Marx, Giorgia Ramponi

The prior is typically specified as a class of parametric distributions, the design of which can be cumbersome in practice, often resulting in the choice of uninformative priors.

1 code implementation • NeurIPS 2023 • Paweł Czyż, Frederic Grabowski, Julia E. Vogt, Niko Beerenwinkel, Alexander Marx

Mutual information is a general statistical dependency measure which has found applications in representation learning, causality, domain generalization and computational biology.

1 code implementation • 16 Mar 2023 • Imant Daunhawer, Alice Bizeul, Emanuele Palumbo, Alexander Marx, Julia E. Vogt

Our work generalizes previous identifiability results by redefining the generative process in terms of distinct mechanisms with modality-specific latent variables.

1 code implementation • 13 Oct 2022 • Alexander Immer, Christoph Schultheiss, Julia E. Vogt, Bernhard Schölkopf, Peter Bühlmann, Alexander Marx

We study the class of location-scale or heteroscedastic noise models (LSNMs), in which the effect $Y$ can be written as a function of the cause $X$ and a noise source $N$ independent of $X$, which may be scaled by a positive function $g$ over the cause, i. e., $Y = f(X) + g(X)N$.

1 code implementation • 13 Jan 2021 • Alexander Marx, Lincen Yang, Matthijs van Leeuwen

Further, we show that CMI can be consistently estimated for discrete-continuous mixture variables by learning an adaptive histogram model.

Causal Discovery Information Theory Information Theory Applications

no code implementations • 27 Oct 2020 • Alexander Marx, Arthur Gretton, Joris M. Mooij

One of the core assumptions in causal discovery is the faithfulness assumption, i. e., assuming that independencies found in the data are due to separations in the true causal graph.

no code implementations • 12 Mar 2019 • Alexander Marx, Jilles Vreeken

Testing for conditional independence is a core aspect of constraint-based causal discovery.

no code implementations • 20 Aug 2018 • Alexander Marx, Jilles Vreeken

We consider the problem of inferring the directed, causal graph from observational data, assuming no hidden confounders.

no code implementations • 26 Sep 2017 • Alexander Marx, Jilles Vreeken

We infer $X$ causes $Y$ in case it is shorter to describe $Y$ as a function of $X$ than the inverse direction.

no code implementations • 21 Feb 2017 • Alexander Marx, Jilles Vreeken

Given data over the joint distribution of two random variables $X$ and $Y$, we consider the problem of inferring the most likely causal direction between $X$ and $Y$.

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