| 2025 | ICML | When to retrain a machine learning model. | Florence Regol, Leo Schwinn, Kyle Sprague, Mark Coates, Thomas Markovich |
| 2024 | AISTATS | Categorical Generative Model Evaluation via Synthetic Distribution Coarsening. | Florence Regol, Mark Coates |
| 2024 | ICLR | Jointly-Learned Exit and Inference for a Dynamic Neural Network. | Florence Regol, Joud Chataoui, Mark Coates |
| 2024 | ICML | Interacting Diffusion Processes for Event Sequence Forecasting. | Mai Zeng, Florence Regol, Mark Coates |
| 2023 | AAAI | Diffusing Gaussian Mixtures for Generating Categorical Data. | Florence Regol, Mark Coates |
| 2023 | ICASSP | Evaluation of Categorical Generative Models - Bridging the Gap Between Real and Synthetic Data. | Florence Regol, Anja Kroon, Mark Coates |
| 2022 | AAAI | Bag Graph: Multiple Instance Learning Using Bayesian Graph Neural Networks. | Soumyasundar Pal, Antonios Valkanas, Florence Regol, Mark Coates |
| 2021 | AISTATS | Detection and Defense of Topological Adversarial Attacks on Graphs. | Yingxue Zhang, Florence Regol, Soumyasundar Pal, Sakif Khan, Liheng Ma, Mark Coates |
| 2020 | ACSSC | Learning from Networks of Distributions. | Antonios Valkanas, Florence Regol, Mark Coates |
| 2020 | ICML | Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation. | Florence Regol, Soumyasundar Pal, Yingxue Zhang, Mark Coates |
| 2020 | KDD | A Framework for Recommending Accurate and Diverse Items Using Bayesian Graph Convolutional Neural Networks. | Jianing Sun, Wei Guo, Dengcheng Zhang, Yingxue Zhang, Florence Regol, Yaochen Hu, Huifeng Guo, Ruiming Tang, Han Yuan, Xiuqiang He, Mark Coates |
| 2020 | UAI | Non Parametric Graph Learning for Bayesian Graph Neural Networks. | Soumyasundar Pal, Saber Malekmohammadi, Florence Regol, Yingxue Zhang, Yishi Xu, Mark Coates |