Node Copying for Protection Against Graph Neural Network Topology Attacks. - Florence Regol; Soumyasundar Pal; Mark Coates (CAMSAP, 2019)

Bayesian Graph Convolutional Neural Networks Using Non-Parametric Graph Learning. - Soumyasundar Pal; Florence Regol; Mark J. Coates (CoRR, 2019)

Bayesian Graph Convolutional Neural Networks using Node Copying. - Soumyasundar Pal; Florence Regol; Mark Coates (CoRR, 2019)

Learning from Networks of Distributions. - Antonios Valkanas; Florence Regol; Mark Coates (ACSSC, 2020)

Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation. - Florence Regol; Soumyasundar Pal; Yingxue Zhang 001; Mark Coates (ICML, 2020)

A Framework for Recommending Accurate and Diverse Items Using Bayesian Graph Convolutional Neural Networks. - Jianing Sun; Wei Guo 006; Dengcheng Zhang; Yingxue Zhang 001; Florence Regol; Yaochen Hu 001; Huifeng Guo; Ruiming Tang; Han Yuan; Xiuqiang He 001; Mark Coates (KDD, 2020)

Non Parametric Graph Learning for Bayesian Graph Neural Networks. - Soumyasundar Pal; Saber Malekmohammadi; Florence Regol; Yingxue Zhang 001; Yishi Xu; Mark Coates (UAI, 2020)

Detection and Defense of Topological Adversarial Attacks on Graphs. - Yingxue Zhang 001; Florence Regol; Soumyasundar Pal; Sakif Khan; Liheng Ma; Mark Coates (AISTATS, 2021)

Bag Graph: Multiple Instance Learning Using Bayesian Graph Neural Networks. - Soumyasundar Pal; Antonios Valkanas; Florence Regol; Mark Coates (AAAI, 2022)

Contrastive Learning for Time Series on Dynamic Graphs. - Yitian Zhang; Florence Regol; Antonios Valkanas; Mark Coates (EUSIPCO, 2022)

Node copying: A random graph model for effective graph sampling. - Florence Regol; Soumyasundar Pal; Jianing Sun; Yingxue Zhang 001; Yanhui Geng; Mark Coates (Signal Process., 2022)

Diffusing Gaussian Mixtures for Generating Categorical Data. - Florence Regol; Mark Coates (AAAI, 2023)

Evaluation of Categorical Generative Models - Bridging the Gap Between Real and Synthetic Data. - Florence Regol; Anja Kroon; Mark Coates (ICASSP, 2023)

Jointly-Learned Exit and Inference for a Dynamic Neural Network : JEI-DNN. - Florence Regol; Joud Chataoui; Mark Coates (CoRR, 2023)

Categorical Generative Model Evaluation via Synthetic Distribution Coarsening. - Florence Regol; Mark Coates (AISTATS, 2024)

Jointly-Learned Exit and Inference for a Dynamic Neural Network. - Florence Regol; Joud Chataoui; Mark Coates (ICLR, 2024)

Interacting Diffusion Processes for Event Sequence Forecasting. - Mai Zeng; Florence Regol; Mark Coates (ICML, 2024)

Predicting Probabilities of Error to Combine Quantization and Early Exiting: QuEE. - Florence Regol; Joud Chataoui; Bertrand Charpentier; Mark Coates; Pablo Piantanida; Stephan Günnemann (CoRR, 2024)

Dynamic layer selection in decoder-only transformers. - Theodore Glavas; Joud Chataoui; Florence Regol; Wassim Jabbour; Antonios Valkanas; Boris N. Oreshkin; Mark Coates (CoRR, 2024)

When to retrain a machine learning model. - Florence Regol; Schwinn Leo; Sprague Kyle; Coates Mark; Markovich Thomas (CoRR, 2025)