27 citations · 132 across the 66 of their papers we have counts for
9 papers · 1 filter
Motif Graph Neural Network
Xuexin Chen, Ruichu Cai, Yuan Fang +3
Graphs can model complicated interactions between entities, which naturally emerge in many important applications. These applications can often be cast into standard graph learning…
CCSL: A Causal Structure Learning Method from Multiple Unknown Environments
Wei Chen, Yunjin Wu, Ruichu Cai +2
Most existing causal structure learning methods assume data collected from one environment and independent and identically distributed (i.i.d.). In some cases, data are collected f…
TEA: A Sequential Recommendation Framework via Temporally Evolving Aggregations
Zijian Li, Ruichu Cai, Fengzhu Wu +4
Sequential recommendation aims to choose the most suitable items for a user at a specific timestamp given historical behaviors. Existing methods usually model the user behavior seq…
Transferable Time-Series Forecasting under Causal Conditional Shift
Zijian Li, Ruichu Cai, Tom Z. J Fu +2
This paper focuses on the problem of semi-supervised domain adaptation for time-series forecasting, which is underexplored in literatures, despite being often encountered in practi…
SADGA: Structure-Aware Dual Graph Aggregation Network for Text-to-SQL
Ruichu Cai, Jinjie Yuan, Boyan Xu +1
The Text-to-SQL task, aiming to translate the natural language of the questions into SQL queries, has drawn much attention recently. One of the most challenging problems of Text-to…
Graph Domain Adaptation: A Generative View
Ruichu Cai, Fengzhu Wu, Zijian Li +3
Recent years have witnessed tremendous interest in deep learning on graph-structured data. Due to the high cost of collecting labeled graph-structured data, domain adaptation is im…
On the Role of Entropy-based Loss for Learning Causal Structures with Continuous Optimization
Weilin Chen, Jie Qiao, Ruichu Cai +1
Causal discovery from observational data is an important but challenging task in many scientific fields. Recently, a method with non-combinatorial directed acyclic constraint, call…
THP: Topological Hawkes Processes for Learning Causal Structure on Event Sequences
Ruichu Cai, Siyu Wu, Jie Qiao +3
Learning causal structure among event types on multi-type event sequences is an important but challenging task. Existing methods, such as the Multivariate Hawkes processes, mostly…
FRITL: A Hybrid Method for Causal Discovery in the Presence of Latent Confounders
Wei Chen, Kun Zhang, Ruichu Cai +4
We consider the problem of estimating a particular type of linear non-Gaussian model. Without resorting to the overcomplete Independent Component Analysis (ICA), we show that under…