27 citations · 49 across the 19 of their papers we have counts for
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cs.LG2023
TNPAR: Topological Neural Poisson Auto-Regressive Model for Learning Granger Causal Structure from Event Sequences
Yuequn Liu, Ruichu Cai, Wei Chen +5
Learning Granger causality from event sequences is a challenging but essential task across various applications. Most existing methods rely on the assumption that event sequences a…
cs.LG2023★ 2 cited
Structural Hawkes Processes for Learning Causal Structure from Discrete-Time Event Sequences
Jie Qiao, Ruichu Cai, Siyu Wu +3
Learning causal structure among event types from discrete-time event sequences is a particularly important but challenging task. Existing methods, such as the multivariate Hawkes p…
cs.LG2023★ 8 cited
Quantitatively Measuring and Contrastively Exploring Heterogeneity for Domain Generalization
Yunze Tong, Junkun Yuan, Min Zhang +4
Domain generalization (DG) is a prevalent problem in real-world applications, which aims to train well-generalized models for unseen target domains by utilizing several source doma…