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Found 11 papers, 5 papers with code
The Mixtures and the Neural Critics: On the Pointwise Mutual Information Profiles of Fine Distributions
Mutual information quantifies the dependence between two random variables and remains invariant under diffeomorphisms.
Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning
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
Mutual information is a general statistical dependency measure which has found applications in representation learning, causality, domain generalization and computational biology.
Identifiability Results for Multimodal Contrastive Learning
Our work generalizes previous identifiability results by redefining the generative process in terms of distinct mechanisms with modality-specific latent variables.
On the Identifiability and Estimation of Causal Location-Scale Noise Models
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., $Y = f(X) + g(X)N$.
Estimating Conditional Mutual Information for Discrete-Continuous Mixtures using Multi-Dimensional Adaptive Histograms
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
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.
Testing Conditional Independence on Discrete Data using Stochastic Complexity
Testing for conditional independence is a core aspect of constraint-based causal discovery.
Causal Discovery by Telling Apart Parents and Children
We consider the problem of inferring the directed, causal graph from observational data, assuming no hidden confounders.
Telling Cause from Effect using MDL-based Local and Global Regression
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
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 $Y$.
