Causal discovery from nonstationary subsample time series is a fundamental yet challenging problem in many scientific domains. In such data, observations are often recorded at a coarse time scale (subsampling). At the same time, the underlying data-generating mechanisms may change over time (nonstationarity), making st...
Wei Chen, Qian Huang, Fu-Hong Wang et al.· Neural Networks· 0 citations
Time series classification underpins applications in healthcare, sensing, and industrial monitoring. Although time series foundation models support forecasting and transferable representation learning, classification still typically requires fitting a task-specific classifier on each target dataset, while individual ch...
Jun Fang, Shi-Feng Xie, Rui-Chu Cai et al.· 0 citations
IADD-TR is proposed, a unified framework combining Intervention-Aware Dynamics Decoupling (IADD) and Targeted Regularization (TR), which achieves competitive returns with improved sample efficiency on five MuJoCo tasks.
Ze-Feng Liang, Jie Qiao, Rui-Chu Cai et al.· 0 citations
The Causal Discovery Foundation Model (CDFM) is formulated as a unified, general-purpose framework for zero-shot structural inference, and a principled variational framework that treats unknown causal mechanisms as latent variables and mathematically decomposes the intractable marginal likelihood into distinct, tractab...
Jie Qiao, Ruichu Cai, Zijian Li et al.· arXiv.org· 2 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
