Two Papers Accepted for KDD 2023 | Machine Learning and Data Mining Research Laboratory

Two papers from our lab were accepted for The 29th SIGKDD Conference on Knowledge Discovery and Data Mining (KDD):

Koh Takeuchi, Ryo Nishida, Hisashi Kashima, Masaki Onishi.
Causal Effect Estimation on Hierarchical Spatial Graph Data.
In Proceedings of the 29th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2023.
- Proposed Spatial Intervention Neural Network (SINet) to estimate causal effects on spatial graph data.

Ryu Shirakami, Toshiya Kitahara, Koh Takeuchi, Hisashi Kashima. QTNet: Theory-based Queue Length Prediction for Urban Traffic.  In Proceedings of the 29th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2023. 
- Proposed Queueing-theory-based Neural Network (QT-Net) to predict traffic queue lengths in urban transportation networks.