Florence Regol

A PhD student from Montreal

About

Welcome to my personal webpage!
I am a 3rd year PhD candidate at McGill University in Montréal, QC under the supervision of Prof. Mark Coates.

Research Interests

Learning uncertainty (generative models, evaluating generative models, Bayesian inference).

Machine learning on graphs (node classification/regression, graph sampling, generative graph model, recommender system).

Active learning.

Mostly anything.

Links

CV
Link to participate in my experiment: active learning for humans

Publications

• F. Regol and M. Coates. (2023). Diffusing gaussian mixtures for generating categorical data. Proc. AAAI Conf. on Artificial Intelligence (AAAI).
• F. Regol, Pal, S., Sun, J., Zhang, Y., Geng, Y., & Coates, M. (2022). Node copying: A random graph model for effective graph sampling. Signal Processing, 192.
• Pal, S., Valkanas, A., F. Regol, & Coates, M. (2022). Bag Graph: Multiple Instance Learning using Bayesian Graph Neural Networks. Proc. AAAI Conf. on Artificial Intelligence.
• F. Regol, Pal, S., & Coates, M. (2019). Node Copying for Protection Against Graph Neural Network Topology Attacks. Proc. IEEE Computational Advances in Multi-Sensor Adaptive Process. (CAMSAP).
• Sun, J., Guo, W., Zhang, D., Zhang, Y., F. Regol, Hu, Y., … Coates, M. (2020). A Framework for Recommending Accurate and Diverse Items Using Bayesian Graph Convolutional Neural Networks. KDD.
• F. Regol, Pal, S., Zhang, Y., & Coates, M. (2020). Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation. Proc. Int. Conf. Machine Learning (ICML).
• Pal, S., Malekmohammadi, S., F. Regol, Zhang, Y., Xu, Y., & Coates, M. (2020). Non Parametric Graph Learning for Bayesian Graph Neural Networks. Proc. Uncertainty in Artificial Intell. (UAI).
• Zhang, Y., F. Regol, Pal, S., Khan, S., Ma, L., & Coates, M. (2021). Detection and Defense of Topological Adversarial Attacks on Graphs. Proc. Int. Conf. on Artificial Intelligence and Statistics (AISTATS).
• Pal, S., F. Regol, & Coates, M. (2019a). Bayesian graph convolutional neural networks using node copying. Proc. Learning and Reasoning with Graph-Structured Representations Workshop, (ICLR).
• Pal, S., F. Regol, & Coates, M. (2019b). Bayesian graph convolutional neural networks using non-parametric graph learning. Proc. Representation Learning on Graphs and Manifolds Workshop (ICML).

Affiliation

Department of Electrical and Computer Engineering
McGill University
McConnell Engineering Building
3480 Rue University, Montréal, QC H3A 0E9

Contact

Email:

florence.robert-regol[at]mail.mcgill.ca