Causal Structure Learning by Using Intersection of Markov Blankets

In this paper, we introduce a novel causal structure learning algorithm called Endogenous and Exogenous Markov Blankets Intersection (EEMBI), which combines the properties of Bayesian networks and Structural Causal Models (SCM). Furthermore, we propose an extended version of EEMBI, namely EEMBI-PC, which integrates the last step of the PC algorithm into EEMBI.

An adaptive dimension reduction algorithm for latent variables of variational autoencoder

Constructed by the neural network, variational autoencoder has the overfitting problem caused by setting too many neural units, we develop an adaptive dimension reduction algorithm that can automatically learn the dimension of latent variable vector, moreover, the dimension of every hidden layer. This approach not only apply to the variational autoencoder but also other variants like Conditional VAE(CVAE), and we show the empirical results on six data sets which presents the universality and efficiency of this algorithm. The key advantages of this algorithm is that it can converge the dimension of latent variable vector which approximates the dimension reaches minimum loss of variational autoencoder(VAE), also speeds up the generating and computing speed by reducing the neural units.