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Found 7 papers, 1 papers with code
Domain Generalization via Multidomain Discriminant Analysis
Domain generalization (DG) aims to incorporate knowledge from multiple source domains into a single model that could generalize well on unseen target domains.
A Kernel Embedding-based Approach for Nonstationary Causal Model Inference
Although nonstationary data are more common in the real world, most existing causal discovery methods do not take nonstationarity into consideration.
Causal Inference and Mechanism Clustering of A Mixture of Additive Noise Models
The inference of the causal relationship between a pair of observed variables is a fundamental problem in science, and most existing approaches are based on one single causal model.
Causal Inference on Discrete Data via Estimating Distance Correlations
In this paper, we deal with the problem of inferring causal directions when the data is on discrete domain.
Confounder Detection in High Dimensional Linear Models using First Moments of Spectral Measures
Based on an assumption of rotation invariant generating process of the model, recent study shows that the spectral measure induced by the regression coefficient vector with respect to the covariance matrix of $X_n$ is close to a uniform measure in purely causal cases, but it differs from a uniform measure characteristically in the presence of a scalar confounder.
Bridging Information Criteria and Parameter Shrinkage for Model Selection
Model selection based on classical information criteria, such as BIC, is generally computationally demanding, but its properties are well studied.
Causal discovery with scale-mixture model for spatiotemporal variance dependencies
In conventional causal discovery, structural equation models (SEM) are directly applied to the observed variables, meaning that the causal effect can be represented as a function of the direct causes themselves.
