Aditya Malik, Nalini Ratha, et al.
CAI 2024
Learning temporal causal graph structures from multivariate time-series data reveals important dependency relationships between current observations and histories, and provides a better understanding of complex systems. In this paper, we examine learning tasks where one is presented with multiple multivariate time-series, as well as a relational graph among the different time-series. We propose an L1 regularized hidden Markov random field regression framework to leverage the information provided by the relational graph and jointly infer more accurate temporal causal structures for all time-series. We test the proposed model on climate modeling and cross-species microarray data analysis applications. Copyright 2010 by the author(s)/owner(s).
Aditya Malik, Nalini Ratha, et al.
CAI 2024
Pavel Klavík, A. Cristiano I. Malossi, et al.
Philos. Trans. R. Soc. A
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NeurIPS 2023
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CVPR 2025