Deep generative models such as Generative Adversarial Networks (GANs) and Variational Auto-Encoders (VAEs) are important tools to capture and investigate the properties of complex empirical data.
We consider linear models where $d$ potential causes $X_1,..., X_d$ are correlated with one target quantity $Y$ and propose a method to infer whether the association is causal or whether it is an artifact caused by overfitting or hidden common causes.
Generative models are important tools to capture and investigate the properties of complex empirical data.
Online social networking sites are experimenting with the following crowd-powered procedure to reduce the spread of fake news and misinformation: whenever a user is exposed to a story through her feed, she can flag the story as misinformation and, if the story receives enough flags, it is sent to a trusted third party for fact checking.
We consider the problem of learning the functions computing children from parents in a Structural Causal Model once the underlying causal graph has been identified.
We study unsupervised generative modeling in terms of the optimal transport (OT) problem between true (but unknown) data distribution $P_X$ and the latent variable model distribution $P_G$.
We study a model where one target variable Y is correlated with a vector X:=(X_1,..., X_d) of predictor variables being potential causes of Y.
Structural Causal Models are widely used in causal modelling, but how they relate to other modelling tools is poorly understood.
Cloud computing involves complex technical and economical systems and interactions.
We show how, and under which conditions, the equilibrium states of a first-order Ordinary Differential Equation (ODE) system can be described with a deterministic Structural Causal Model (SCM).
We propose one-class support measure machines (OCSMMs) for group anomaly detection which aims at recognizing anomalous aggregate behaviors of data points.
Can we recover the hidden network structures from these observed cascades?
Cryo-electron microscopy (cryo-EM) is an emerging experimental method to characterize the structure of large biomolecular assemblies.
We propose a kernel method to identify finite mixtures of nonparametric product distributions.
We consider the problem of function estimation in the case where an underlying causal model can be inferred.
We consider two variables that are related to each other by an invertible function.
Conditional independence testing is an important problem, especially in Bayesian network learning and causal discovery.
We explain why a consistent reformulation of causal inference in terms of algorithmic complexity implies a new inference principle that takes into account also the complexity of conditional probability densities, making it possible to select among Markov equivalent causal graphs.