About
Algorithms and theory for probabilistic inference and generative modelling: induction of compositional structure in generative models, modelling of posteriors over high-dimensional explanatory variables, and Bayesian neurosymbolic methods for reasoning in language and formal systems.
Relevant Publications
- Bengio, Y., & Malkin, N. (2024). Machine learning and information theory concepts towards an AI mathematician. Bulletin of the American Mathematical Society, 61(3), 457-469.
- Hu, E. J., Jain, M., Elmoznino, E., Kaddar, Y., Lajoie, G., Bengio, Y., & Malkin, N. (2024). Amortizing intractable inference in large language models. The Twelfth International Conference on Learning Representations.
- Lahlou, S., Deleu, T., Lemos, P., Zhang, D., Volokhova, A., Hernández-Garcı́a, A., Ezzine, L.N., Bengio, Y. & Malkin, N.. (2023). A theory of continuous generative flow networks. Proceedings of the 40th International Conference on Machine Learning, in Proceedings of Machine Learning Research 202:18269-18300.