Pierre-André Noël
Applied Research Scientist
Low Data Learning
Pierre-André Noël is an Applied Research Scientist at ServiceNow Research. His recent work involves Graph Neural Networks, Self-Supervised Learning, Relational Databases, Knowledge Graphs and/or Constrained Inference. He holds a PhD in Physics from Université Laval and was a postdoctoral researcher at University of California Davis. His doctoral and postdoctoral research pertained to Complex Networks, Statistical Mechanics and Stochastic Processes. In 2017, he joined an NLP team at Element AI where he spent the following years building core capabilities and working on special projects. ServiceNow acquired Element AI in 2021, and Pierre-André now focuses on fundamental research, with an applied twist.
Interests
- Relational Models
- Diffusion Models
- Generative Models
- Theory of Machine Learning
Publications
Constraining Representations Yields Models That Know What They Don't Know.
International Conference of Learning Representations (ICLR),
2023.
Flaky Performances when Pretraining on Relational Databases.
AAAI-23 Student Abstract and Poster Program,
2023.
Constraining Low-level Representations to Define Effective Confidence Scores.
Workshop at the Neural Information Processing Systems (NeurIPS),
2022.
Exploring the Design Space of Generative Diffusion Processes for Sparse Graphs.
Workshop at the Neural Information Processing Systems (NeurIPS),
2022.
Leveraging Activation Patterns to Define Classifiers Able to Detect and Reject Anomalies.
Montreal AI Symposium (MAIS),
2022.
Flaky Performances when Pre-Training on Relational Databases with a Plan for Future Characterization Efforts.
Workshop at the International Conference on Machine Learning (ICML),
2022.
On the Value of ML Models.
Workshop at the Neural Information Processing Systems (NeurIPS),
2021.