Ioannis Mitliagkas is a Canada CIFAR AI Chair at Mila and an assistant professor in the Department of Computer Science and Operations Research (DIRO) at Université de Montréal.
Mitliagkas’ research focuses on broad-scale statistical learning and inference problems, focusing on efficient broad-scale and distributed algorithms, and the tight theoretical and data-dependent guarantees and tuning complex systems. His recent work includes understanding and optimizing the scanning used in Gibbs sampling for inference, as well as understanding the interaction between optimization and the dynamics of large-scale learning systems.
- Best Student Paper Award, OPT, 2020
Loizou, N., Berard, H., Gidel, G., Mitliagkas, I., & Lacoste-Julien, S. (2021). Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity.
Ahuja, K., Caballero, E., Zhang, D., Bengio, Y., Mitliagkas, I., & Rish, I. (2021). Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization.
Jolicoeur-Martineau, A., Li, K., Piché-Taillefer, R., Kachman, T., & Mitliagkas, I. (2021). Gotta Go Fast When Generating Data with Score-Based Models.
Jolicoeur-Martineau, A., Piché-Taillefer, R., Combes, R. T. D., & Mitliagkas, I. (2020). Adversarial score matching and improved sampling for image generation.
Jolicoeur-Martineau, A., & Mitliagkas, I. (2019). Gradient penalty from a maximum margin perspective.
CIFAR is a registered charitable organization supported by the governments of Canada, Alberta and Quebec, as well as foundations, individuals, corporations and Canadian and international partner organizations.