no code implementations • 3 Mar 2022 • Francois St-Hilaire, Dung Do Vu, Antoine Frau, Nathan Burns, Farid Faraji, Joseph Potochny, Stephane Robert, Arnaud Roussel, Selene Zheng, Taylor Glazier, Junfel Vincent Romano, Robert Belfer, Muhammad Shayan, Ariella Smofsky, Tommy Delarosbil, Seulmin Ahn, Simon Eden-Walker, Kritika Sony, Ansona Onyi Ching, Sabina Elkins, Anush Stepanyan, Adela Matajova, Victor Chen, Hossein Sahraei, Robert Larson, Nadia Markova, Andrew Barkett, Laurent Charlin, Yoshua Bengio, Iulian Vlad Serban, Ekaterina Kochmar
AI-powered learning can provide millions of learners with a highly personalized, active and practical learning experience, which is key to successful learning.
no code implementations • 15 Apr 2021 • Francois St-Hilaire, Nathan Burns, Robert Belfer, Muhammad Shayan, Ariella Smofsky, Dung Do Vu, Antoine Frau, Joseph Potochny, Farid Faraji, Vincent Pavero, Neroli Ko, Ansona Onyi Ching, Sabina Elkins, Anush Stepanyan, Adela Matajova, Laurent Charlin, Yoshua Bengio, Iulian Vlad Serban, Ekaterina Kochmar
Personalization and active learning are key aspects to successful learning.
1 code implementation • ICML 2020 • Avishek Joey Bose, Ariella Smofsky, Renjie Liao, Prakash Panangaden, William L. Hamilton
One effective solution is the use of normalizing flows \cut{defined on Euclidean spaces} to construct flexible posterior distributions.
1 code implementation • 6 Jun 2019 • Patrick Nadeem Ward, Ariella Smofsky, Avishek Joey Bose
Deep Reinforcement Learning (DRL) algorithms for continuous action spaces are known to be brittle toward hyperparameters as well as \cut{being}sample inefficient.