Gauthier Gidel’s research lies at the intersection between learning, game theory and optimization. He aims to build a better understanding of adversarial formulations for machine learning (ML). He’s interested in the fundamental reasons behind the great successes of adversarial formulations and at efficient training methods in such an adversarial context.
- Borealis AI graduate fellowship (2019)
- DIRO excellence grant (2017 and 2018)
- Bose, A. J., Gidel, G., Berrard, H., Cianflone, A., Vincent, P., Lacoste-Julien, S., & Hamilton, W. L. (2020). Adversarial Example Games. arXiv preprint arXiv:2007.00720.
- Czarnecki, W. M., Gidel, G., Tracey, B., Tuyls, K., Omidshafiei, S., Balduzzi, D., & Jaderberg, M. (2020). Real World Games Look Like Spinning Tops. arXiv preprint arXiv:2004.09468.
- Gidel, G., Berard, H., Vignoud, G., Vincent, P., & Lacoste-Julien, S. (2019). A variational inequality perspective on generative adversarial networks. arXiv preprint arXiv:1802.10551.
- Gidel, G., Hemmat, R. A., Pezeshki, M., Le Priol, R., Huang, G., Lacoste-Julien, S., & Mitliagkas, I. (2019, April). Negative momentum for improved game dynamics. In The 22nd International Conference on Artificial Intelligence and Statistics (pp. 1802-1811). PMLR.
- Chavdarova, T., Gidel, G., Fleuret, F., & Lacoste-Julien, S. (2019). Reducing noise in gan training with variance reduced extragradient. In Advances in Neural Information Processing Systems (pp. 393-403).
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.