Search Results for author: Alexander Marx

Found 11 papers, 5 papers with code

Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning

no code implementations11 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.


Beyond Normal: On the Evaluation of Mutual Information Estimators

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.

Benchmarking Domain Generalization +1

Identifiability Results for Multimodal Contrastive Learning

1 code implementation16 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.

Contrastive Learning Representation Learning

On the Identifiability and Estimation of Causal Location-Scale Noise Models

1 code implementation13 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., $Y = f(X) + g(X)N$.

Causal Discovery Causal Inference

Estimating Conditional Mutual Information for Discrete-Continuous Mixtures using Multi-Dimensional Adaptive Histograms

1 code implementation13 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

A Weaker Faithfulness Assumption based on Triple Interactions

no code implementations27 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.

Causal Discovery

Testing Conditional Independence on Discrete Data using Stochastic Complexity

no code implementations12 Mar 2019 Alexander Marx, Jilles Vreeken

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

Causal Discovery

Causal Discovery by Telling Apart Parents and Children

no code implementations20 Aug 2018 Alexander Marx, Jilles Vreeken

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

Causal Discovery

Telling Cause from Effect using MDL-based Local and Global Regression

no code implementations26 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.


Causal Inference on Multivariate and Mixed-Type Data

no code implementations21 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 $Y$.

Causal Inference Vocal Bursts Type Prediction

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