Supervised Mixture Models for Population Health. - Xiao Shou; Georgios Mavroudeas; Alexander New; Kofi Arhin; Jason Nicholas Kuruzovich; Malik Magdon-Ismail; Kristin P. Bennett (BIBM, 2019)

Match2: hybrid self-organizing map and deep learning strategies for treatment effect estimation. - Xiao Shou; Tian Gao; Dharmashankar Subramanian; Kristin P. Bennett (BCB, 2021)

Predictive Modeling for Complex Care Management. - Georgios Mavroudeas; Nafis Neehal; Xiao Shou; Malik Magdon-Ismail; Jason Nicholas Kuruzovich; Kristin P. Bennett (BIBM, 2021)

Causal Inference for Event Pairs in Multivariate Point Processes. - Tian Gao; Dharmashankar Subramanian; Debarun Bhattacharjya; Xiao Shou; Nicholas Mattei; Kristin P. Bennett (NeurIPS, 2021)

HMM-Boost: Improved Time Series State Prediction Via Supervised Hidden Markov Models: Case Studies in Epileptic Seizure and Complex Care Management. - Georgios Mavroudeas; Malik Magdon-Ismail; Xiao Shou; Kristin P. Bennett (ICDM (Workshops), 2022)

Score-Based Learning of Graphical Event Models with Background Knowledge Augmentation. - Debarun Bhattacharjya; Tian Gao; Dharmashankar Subramanian; Xiao Shou (AAAI, 2023)

Concurrent Multi-Label Prediction in Event Streams. - Xiao Shou; Tian Gao; Dharmashankar Subramanian; Debarun Bhattacharjya; Kristin P. Bennett (AAAI, 2023)

Influence-Aware Attention for Multivariate Temporal Point Processes. - Xiao Shou; Tian Gao; Dharmashankar Subramanian; Debarun Bhattacharjya; Kristin P. Bennett (CLeaR, 2023)

Probabilistic Attention-to-Influence Neural Models for Event Sequences. - Xiao Shou; Debarun Bhattacharjya; Tian Gao; Dharmashankar Subramanian; Oktie Hassanzadeh; Kristin P. Bennett (ICML, 2023)

Pairwise Causality Guided Transformers for Event Sequences. - Xiao Shou; Debarun Bhattacharjya; Tian Gao; Dharmashankar Subramanian; Oktie Hassanzadeh; Kristin P. Bennett (NeurIPS, 2023)

Self-Supervised Contrastive Pre-Training for Multivariate Point Processes. - Xiao Shou; Dharmashankar Subramanian; Debarun Bhattacharjya; Tian Gao; Kristin P. Bennett (CoRR, 2024)

Architecture-Aware Learning Curve Extrapolation via Graph Ordinary Differential Equation. - Yanna Ding; Zijie Huang 002; Xiao Shou; Yihang Guo; Yizhou Sun; Jianxi Gao (AAAI, 2025)

Less is More: Efficient Weight Farcasting with 1-Layer Neural Network. - Xiao Shou; Debarun Bhattacharjya; Yanna Ding; Chen Zhao; Rui Li; Jianxi Gao (CoRR, 2025)

Gradient Flow Matching for Learning Update Dynamics in Neural Network Training. - Xiao Shou; Yanna Ding; Jianxi Gao (CoRR, 2025)