Portrait de Audrey Durand

Audrey Durand

Membre académique associé
Chaire en IA Canada-CIFAR
Professeure adjointe, Université Laval, Département d'informatique et de génie logiciel
Sujets de recherche
Apprentissage en ligne
Apprentissage par renforcement
IA pour la science
Site web
Google Scholar
LinkedIn

Biographie

Audrey Durand est professeure adjointe au Département d’informatique et de génie logiciel ainsi qu’au Département de génie électrique et de génie informatique de l’Université Laval. Elle se spécialise dans les algorithmes qui apprennent par l’interaction avec leur environnement, soit l’apprentissage par renforcement, et s’intéresse particulièrement à l’application de ces approches au domaine de la santé.

Étudiants actuels

Julien Armand
Maîtrise recherche - Université Laval
Yohaï-Eliel Berreby
Doctorat - McGill
Site web
Github
Google Scholar
Baptiste Bonin
Maîtrise recherche - Université Laval
Hugo Delhaye
Maîtrise recherche - UdeM
Superviseur⋅e principal⋅e :
Lune Bellec
Maxime Heuillet
Doctorat - Université Laval
Bruno Joyal
Maîtrise recherche - Université Laval
Alexandre Larouche
Doctorat - Université Laval
Github
Google Scholar
Randy Lefebvre
Doctorat - Université Laval
Hadi Moazen
Doctorat - Université Laval
Caro Strickland
Postdoctorat - Université Laval
Site web
Github
Google Scholar

Publications

Streaming kernel regression with provably adaptive mean, variance, and regularization
Audrey Durand
Odalric-Ambrym Maillard
Joelle Pineau
We consider the problem of streaming kernel regression, when the observations arrive sequentially and the goal is to recover the underlying … (voir plus)mean function, assumed to belong to an RKHS. The variance of the noise is not assumed to be known. In this context, we tackle the problem of tuning the regularization parameter adaptively at each time step, while maintaining tight confidence bounds estimates on the value of the mean function at each point. To this end, we first generalize existing results for finite-dimensional linear regression with fixed regularization and known variance to the kernel setup with a regularization parameter allowed to be a measurable function of past observations. Then, using appropriate self-normalized inequalities we build upper and lower bound estimates for the variance, leading to Bersntein-like concentration bounds. The later is used in order to define the adaptive regularization. The bounds resulting from our technique are valid uniformly over all observation points and all time steps, and are compared against the literature with numerical experiments. Finally, the potential of these tools is illustrated by an application to kernelized bandits, where we revisit the Kernel UCB and Kernel Thompson Sampling procedures, and show the benefits of the novel adaptive kernel tuning strategy.
2017-12-31
Journal of Machine Learning Research (publié)
arxiv.org
arxiv.org
Temporal Regularization for Markov Decision Process
Pierre Thodoroff
Audrey Durand
Joelle Pineau
Doina Precup
Several applications of Reinforcement Learning suffer from instability due to high variance. This is especially prevalent in high dimensiona… (voir plus)l domains. Regularization is a commonly used technique in machine learning to reduce variance, at the cost of introducing some bias. Most existing regularization techniques focus on spatial (perceptual) regularization. Yet in reinforcement learning, due to the nature of the Bellman equation, there is an opportunity to also exploit temporal regularization based on smoothness in value estimates over trajectories. This paper explores a class of methods for temporal regularization. We formally characterize the bias induced by this technique using Markov chain concepts. We illustrate the various characteristics of temporal regularization via a sequence of simple discrete and continuous MDPs, and show that the technique provides improvement even in high-dimensional Atari games.
2017-12-31
Advances in Neural Information Processing Systems 31 (NeurIPS 2018) (publié)
dblp.uni-trier.de