| A framework for recommending accurate and diverse items using bayesian graph convolutional neural networks J Sun, W Guo, D Zhang, Y Zhang, F Regol, Y Hu, H Guo, R Tang, H Yuan, ... Proceedings of the 26th ACM SIGKDD international conference on knowledge …, 2020 | 83 | 2020 |
| Bayesian graph convolutional neural networks using node copying S Pal, F Regol, M Coates arXiv preprint arXiv:1911.04965, 2019 | 22 | 2019 |
| Bag graph: Multiple instance learning using bayesian graph neural networks S Pal, A Valkanas, F Regol, M Coates Proceedings of the AAAI Conference on Artificial Intelligence 36 (7), 7922-7930, 2022 | 18 | 2022 |
| Non parametric graph learning for Bayesian graph neural networks S Pal, S Malekmohammadi, F Regol, Y Zhang, Y Xu, M Coates Conference on uncertainty in artificial intelligence, 1318-1327, 2020 | 18 | 2020 |
| Bayesian graph convolutional neural networks using non-parametric graph learning S Pal, F Regol, M Coates arXiv preprint arXiv:1910.12132, 2019 | 14 | 2019 |
| Active learning on attributed graphs via graph cognizant logistic regression and preemptive query generation F Regol, S Pal, Y Zhang, M Coates International Conference on Machine Learning, 8041-8050, 2020 | 13 | 2020 |
| Detection and defense of topological adversarial attacks on graphs Y Zhang, F Regol, S Pal, S Khan, L Ma, M Coates International Conference on Artificial Intelligence and Statistics, 2989-2997, 2021 | 9 | 2021 |
| Node copying: A random graph model for effective graph sampling F Regol, S Pal, J Sun, Y Zhang, Y Geng, M Coates Signal Processing 192, 108335, 2022 | 5 | 2022 |
| Diffusing gaussian mixtures for generating categorical data F Regol, M Coates Proceedings of the AAAI Conference on Artificial Intelligence 37 (8), 9570-9578, 2023 | 3 | 2023 |
| Jointly-learned exit and inference for a dynamic neural network: Jei-dnn F Regol, J Chataoui, M Coates arXiv preprint arXiv:2310.09163, 2023 | 2 | 2023 |
| Evaluation of Categorical Generative Models-Bridging the Gap Between Real and Synthetic Data F Regol, A Kroon, M Coates ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and …, 2023 | 2 | 2023 |
| Learning from networks of distributions A Valkanas, F Regol, M Coates 2020 54th Asilomar Conference on Signals, Systems, and Computers, 574-578, 2020 | 2 | 2020 |
| Node copying for protection against graph neural network topology attacks F Regol, S Pal, M Coates 2019 IEEE 8th International Workshop on Computational Advances in Multi …, 2019 | 1 | 2019 |
| Predicting Probabilities of Error to Combine Quantization and Early Exiting: QuEE F Regol, J Chataoui, B Charpentier, M Coates, P Piantanida, ... arXiv preprint arXiv:2406.14404, 2024 | | 2024 |
| Categorical Generative Model Evaluation via Synthetic Distribution Coarsening F Regol, M Coates International Conference on Artificial Intelligence and Statistics, 910-918, 2024 | | 2024 |
| Interacting Diffusion Processes for Event Sequence Forecasting M Zeng, F Regol, M Coates arXiv preprint arXiv:2310.17800, 2023 | | 2023 |
| Contrastive Learning for Time Series on Dynamic Graphs Y Zhang, F Regol, A Valkanas, M Coates 2022 30th European Signal Processing Conference (EUSIPCO), 742-746, 2022 | | 2022 |
| GEEM: An algorithm for Active Learning on Attributed Graphs F Regol, S Pal, Y Zhang, M Coates | | 2020 |
| Active Learning on Graphs-Sampling the Initial Set F Regol | | |
