Selected publications with code below.
Naiyu Yin, Tian Gao, Yue Yu, and Qiang Ji. "Effective Causal Discovery under Identifiable Heteroscedastic Noise Model". AAAI 2024. [Paper] [Code]
Yue Yu, Tian Gao, Naiyu Yin, and Qiang Ji. "DAGs with No Curl: An Efficient DAG Structure Learning Approach". ICML 2021. [Paper] [Code]
Rui Chen, Sanjeeb Dash, and Tian Gao. "Integer programming for causal structure learning in the presence of latent variables". ICML 2021. [Paper] [Code]
Dennis Wei, Tian Gao, and Yue Yu. ""DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks." NeurIPS 2020. [Paper] [Code]
Yue Yu, Jie Chen, Tian Gao, and Mo Yu. "DAG-GNN: DAG Structure Learning with Graph Neural Networks". ICML 19. [Paper] [Code]
Xiao Shou, Tian Gao, Dharmashankar Subramanian, Kristin P. Bennett. "Match 2: Hybrid Self-Organizing Map and Deep Learning Strategies for Treatment Effect Estimation." ACM-BCB 2021, Best Paper Award. [Paper] [Code]
Xiao Shou, Debarun Bhattacharjya, Tian Gao, Dharmashankar Subramanian, Oktie Hassanzadeh, Kristin P. Bennett. "Probabilistic Attention-to-Influence Neural Models for Event Sequences." ICML 2023. [Paper] [Code]
Xiao Shou, Debarun Bhattacharjya, Tian Gao, Dharmashankar Subramanian, Oktie Hassanzadeh, Kristin P. Bennett. "Pairwise Causality Guided Transformers for Event Sequences." NeurIPS 2023. [Paper] [Code]
Xiao Shou, Tian Gao, Dharmashankar Subramanian, Debarun Bhattacharjya, Kristin P. Bennett. "Concurrent Multi-Label Prediction in Event Streams". AAAI 2023. [Paper] [Code]
Zijun Cui, Hanjing Wang, Tian Gao, Kartik Talamadupula, Qiang Ji. "Theory-guided Message Passing Neural Network for Probabilistic Inference". AISTATS 2024. [Paper] [Code]
Zijun Cui, Pavan Kapanipathi, Kartik Talamadupula, Tian Gao, Qiang Ji. "Type-augmented Relation Prediction in Knowledge Graphs". AAAAI 2021. [Paper] [Code]
Dennis Wei, Sanjeeb Dash, Tian Gao, Oktay Günlük. "Generalized linear rule models". ICML 2019. [Paper] [Code], part of IBM AIX360 Toolbox