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Found 10 papers, 1 papers with code
A Theoretical Analysis of the BDeu Scores in Bayesian Network Structure Learning
In Bayesian network structure learning (BNSL), we need the prior probability over structures and parameters.
Causal Discovery in a Binary Exclusive-or Skew Acyclic Model: BExSAM
Discovering causal relations among observed variables in a given data set is a major objective in studies of statistics and artificial intelligence.
Identifiability of an Integer Modular Acyclic Additive Noise Model and its Causal Structure Discovery
The notion of causality is used in many situations dealing with uncertainty.
Converting ADMM to a Proximal Gradient for Efficient Sparse Estimation
In sparse estimation, such as fused lasso and convex clustering, we apply either the proximal gradient method or the alternating direction method of multipliers (ADMM) to solve the problem.
Efficient proximal gradient algorithms for joint graphical lasso
We consider learning an undirected graphical model from sparse data.
Causal Order Identification to Address Confounding: Binary Variables
In particular, we assume that the variables are binary.
Dropout Drops Double Descent
Our paper posits that the optimal test error, in terms of the dropout rate, shows a monotonic decrease in linear regression with increasing sample size.
Functional Linear Non-Gaussian Acyclic Model for Causal Discovery
To expand this concept, we extend the notion of variables to encompass vectors and even functions, leading to the Functional Linear Non-Gaussian Acyclic Model (Func-LiNGAM).
Generalization of LiNGAM that allows confounding
LiNGAM determines the variable order from cause to effect using additive noise models, but it faces challenges with confounding.
Learning under Singularity: An Information Criterion improving WBIC and sBIC
It incorporates the empirical loss from the Widely Applicable Information Criterion (WAIC) to represent the goodness of fit to the statistical model, along with a penalty term similar to that of sBIC.
