Mining of switching sparse networks for missing value imputation in multivariate time series
Multivariate time series data suffer from the problem of missing values, which hinders the
application of many analytical methods. To achieve the accurate imputation of these missing …
application of many analytical methods. To achieve the accurate imputation of these missing …
Interpretable Imitation Learning with Dynamic Causal Relations
Imitation learning, which learns agent policy by mimicking expert demonstration, has shown
promising results in many applications such as medical treatment regimes and self-driving …
promising results in many applications such as medical treatment regimes and self-driving …
Graphical models and dynamic latent factors for modeling functional brain connectivity
With modern technology, the activity of thousands of neurons in the brain can be recorded
simultaneously. Such data can potentially shed light on how neurons communicate with one …
simultaneously. Such data can potentially shed light on how neurons communicate with one …
GGLasso--a Python package for General Graphical Lasso computation
F Schaipp, CL Müller, O Vlasovets - ar** and distinct variables
X Yang, B Niu, T Lan, C Zhang - IISE Transactions, 2025 - Taylor & Francis
Bayesian Network (BN) is a powerful tool for causal dependence relationship discoveries of
multivariate data. This article proposes a federated multi-task learning framework for BNs …
multivariate data. This article proposes a federated multi-task learning framework for BNs …
Dynamic Multi-Network Mining of Tensor Time Series
Subsequence clustering of time series is an essential task in data mining, and interpreting
the resulting clusters is also crucial since we generally do not have prior knowledge of the …
the resulting clusters is also crucial since we generally do not have prior knowledge of the …
Online directed-structural change-point detection: A segment-wise time-varying dynamic Bayesian network approach
High-dimensional data streams exist in many applications. Generally these high-
dimensional streaming data have complex directed conditional dependence relationships …
dimensional streaming data have complex directed conditional dependence relationships …