Probabilistic time series forecasting seeks to quantify the uncertainty of future observations. While recent works introduce latent variables to alleviate the spurious dependencies caused by hidden confounders, thereby reducing overly wide confidence intervals, simply incorporating latent factors is not sufficient. Whe...
Chang-Ze Zhou, Ruichu Cai, Sheng Nie et al.· Proceedings of the Thirty-Fi...· 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, Ruichu Cai et al.· 0 citations
A condition called the cross-Hessian Rank Constraint (HRC), which serves as a primitive rank-based tool for nonlinear latent causal discovery, shows that a rank-based property arises from the cross-Hessian of the observed-data log-density in the nonlinear case, revealing information about the latent variables.
Zijian Li, Ruichu Cai, Feng Xie et al.· 0 citations
Causal Sufficient and Necessary Regional Explanations (SNRE), a framework that learns an input region and output region such that membership in A is both sufficient and necessary for the model output to fall in B, is proposed.
Xuexin Chen, Peng Liang, Zijian Li et al.· 0 citations
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