Publications

Navigation in the Service of Enhanced Pose Estimation
Travis Manderson
Ran Cheng
David Meger
Gregory Dudek
2020-01-22
Springer Proceedings in Advanced Robotics (published)
doi.org
Autism spectrum disorder
Catherine Lord
Traolach S. Brugha
Tony Charman
James Cusack
Guillaume Dumas
Thomas Frazier
Emily J. H. Jones
Rebecca M. Jones
Andrew Pickles
Matthew W. State
Julie Lounds Taylor
Jeremy Veenstra-VanderWeele
2020-01-15
Nature Reviews Disease Primers (published)
doi.org
Optogenetic activation of parvalbumin and somatostatin interneurons selectively restores theta-nested gamma oscillations and oscillation-induced spike timing-dependent long-term potentiation impaired by amyloid β oligomers
Kyerl Park
Jaedong Lee
Hyun Jae Jang
Blake Aaron Richards
Michael M Kohl
Jeehyun Kwag
2020-01-14
BMC Biology (published)
doi.org
Conducting gender-based analysis of existing databases when self-reported gender data are unavailable: the GENDER Index in a working population
M. Gabrielle Pagé
Bilkis Vissandjée
Hermine Lore Nguena Nguefack
Joel Katz
Oumar Mallé Samb
Alain Gillian Lucie David Manon Catherine Anaïs Benoit Alexandre Amélie Pasquale Valérie Marie-Pascale Mike Anne-Marie Marc Josiane Mireille Stéphanie Pierre Annie Isabelle Danielle Denis Jaime André Geneviève Jean-François Roxanne Marc-Antoine Pier Sonia Vanasse
Alain Gillian Lucie David Manon Catherine Anaïs Benoit A Vanasse Bartlett Blais Buckeridge Choinière Hudon
Alain Vanasse
Gillian Bartlett
Lucie Blais
David L Buckeridge
Manon Choinière
Catherine Hudon
Anaïs Lacasse
Benoit Lamarche
Alexandre Lebel
Amélie Quesnel-Vallée
Pasquale Roberge
Valérie Émond
Marie-Pascale Pomey … (see 19 more)
Mike Benigeri
Anne-Marie Cloutier
Marc Dorais
Josiane Courteau
Mireille Courteau
Stéphanie Plante
Pierre Cambon
Annie Giguère
Isabelle Leroux
Danielle St-Laurent
Denis Roy
Jaime Borja
André Néron
Geneviève Landry
Jean-François Ethier
Roxanne Dault
Marc-Antoine Côté-Marcil
Pier Tremblay
Sonia Quirion
Objectives Growing attention has been given to considering sex and gender in health research. However, this remains a challenge in the conte… (see more)xt of retrospective studies where self-reported gender measures are often unavailable. This study aimed to create and validate a composite gender index using data from the Canadian Community Health Survey (CCHS). Methods According to scientific literature and expert opinion, the GENDER Index was built using several variables available in the CCHS and deemed to be gender-related (e.g., occupation, receiving child support, number of working hours). Among workers aged 18–50 years who had no missing data for our variables of interest ( n  = 29,470 participants), propensity scores were derived from a logistic regression model that included gender-related variables as covariates and where biological sex served as the dependent variable. Construct validity of propensity scores (GENDER Index scores) were then examined. Results When looking at the distribution of the GENDER Index scores in males and females, they appeared related but partly independent. Differences in the proportion of females appeared between groups categorized according to the GENDER Index scores tertiles ( p   0.0001). Construct validity was also examined through associations between the GENDER Index scores and gender-related variables identifi
2020-01-12
Canadian journal of public health (published)
doi.org
Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization
Paul Barde
Julien Roy
Wonseok Jeon
Joelle Pineau
Christopher Pal
Derek Nowrouzezahrai
Adversarial Imitation Learning alternates between learning a discriminator -- which tells apart expert's demonstrations from generated ones … (see more)-- and a generator's policy to produce trajectories that can fool this discriminator. This alternated optimization is known to be delicate in practice since it compounds unstable adversarial training with brittle and sample-inefficient reinforcement learning. We propose to remove the burden of the policy optimization steps by leveraging a novel discriminator formulation. Specifically, our discriminator is explicitly conditioned on two policies: the one from the previous generator's iteration and a learnable policy. When optimized, this discriminator directly learns the optimal generator's policy. Consequently, our discriminator's update solves the generator's optimization problem for free: learning a policy that imitates the expert does not require an additional optimization loop. This formulation effectively cuts by half the implementation and computational burden of Adversarial Imitation Learning algorithms by removing the Reinforcement Learning phase altogether. We show on a variety of tasks that our simpler approach is competitive to prevalent Imitation Learning methods.
2019-12-31
Advances in Neural Information Processing Systems 33 (NeurIPS 2020) (published)
doi.org
arxiv.org
Block planning for intermodal rail: Methodology and case study
Gianluca Morganti
T. Crainic
Emma Frejinger
Nicolettta Ricciardi
2019-12-31
Transportation Research Procedia (published)
doi.org
On Bonus-Based Exploration Methods in the Arcade Learning Environment
Adrien Ali Taiga
William Fedus
Marlos C. Machado
Aaron Courville
Bellemare Marc-Emmanuel
Research on exploration in reinforcement learning, as applied to Atari 2600 game-playing, has emphasized tackling difficult exploration prob… (see more)lems such as Montezuma's Revenge (Bellemare et al., 2016). Recently, bonus-based exploration methods, which explore by augmenting the environment reward, have reached above-human average performance on such domains. In this paper we reassess popular bonus-based exploration methods within a common evaluation framework. We combine Rainbow (Hessel et al., 2018) with different exploration bonuses and evaluate its performance on Montezuma's Revenge, Bellemare et al.'s set of hard of exploration games with sparse rewards, and the whole Atari 2600 suite. We find that while exploration bonuses lead to higher score on Montezuma's Revenge they do not provide meaningful gains over the simpler
2019-12-31
ICLR.cc/2020/Conference (poster)
doi.org
openreview.net
Call for Papers: Novel Informatics Approaches to COVID-19 Research
Huanan Xu
David L Buckeridge
Yi Wang
2019-12-31
(published)
www.semanticscholar.org
Coping With Simulators That Don't Always Return
Andrew Warrington
Saeid Naderiparizi
Frank N. Wood
2019-12-31
AISTATS (published)
proceedings.mlr.press
proceedings.mlr.press
COVI White Paper - Version 1.1
Hannah Alsdurf
Yoshua Bengio
Tristan Deleu
Prateek Gupta
Daphne Ippolito
Richard Janda
Max Jarvies
Tyler Kolody
Sekoul Krastev
Tegan Maharaj
Robert Obryk
Dan Pilat
Valérie Pisano
Benjamin Prud'homme
Meng Qu
Nasim Rahaman
Irina Rish
Jean-François Rousseau
Abhinav Sharma
Brooke Struck … (see 3 more)
Jian Tang
Martin Weiss
Yun William Yu
The SARS-CoV-2 (Covid-19) pandemic has caused significant strain on public health institutions around the world. Contact tracing is an essen… (see more)tial tool to change the course of the Covid-19 pandemic. Manual contact tracing of Covid-19 cases has significant challenges that limit the ability of public health authorities to minimize community infections. Personalized peer-to-peer contact tracing through the use of mobile apps has the potential to shift the paradigm. Some countries have deployed centralized tracking systems, but more privacy-protecting decentralized systems offer much of the same benefit without concentrating data in the hands of a state authority or for-profit corporations. Machine learning methods can circumvent some of the limitations of standard digital tracing by incorporating many clues and their uncertainty into a more graded and precise estimation of infection risk. The estimated risk can provide early risk awareness, personalized recommendations and relevant information to the user. Finally, non-identifying risk data can inform epidemiological models trained jointly with the machine learning predictor. These models can provide statistical evidence for the importance of factors involved in disease transmission. They can also be used to monitor, evaluate and optimize health policy and (de)confinement scenarios according to medical and economic productivity indicators. However, such a strategy based on mobile apps and machine learning should proactively mitigate potential ethical and privacy risks, which could have substantial impacts on society (not only impacts on health but also impacts such as stigmatization and abuse of personal data). Here, we present an overview of the rationale, design, ethical considerations and privacy strategy of `COVI,' a Covid-19 public peer-to-peer contact tracing and risk awareness mobile application developed in Canada.
2019-12-31
arXiv (preprint)
doi.org
arxiv.org
COVI White Paper-Version 1.1
H. Alsdurf
Yoshua Bengio
T. Deleu
Prateek Gupta
Daphne Ippolito
R. Janda
Max Jarvie
Tyler Kolody
S. Krastev
Tegan Maharaj
Robert Obryk
D. Pilat
Valérie Pisano
Benjamin Prud'homme
Meng Qu
Nasim Rahaman
I. Rish
J. Rousseau
Abhinav Sharma
B. Struck … (see 3 more)
Jian Tang
Martin Weiss
Yun William Yu
2019-12-31
(published)
www.semanticscholar.org
Cross-layer communication over fading channels with adaptive decision feedback
Borna Sayedana
Aditya Mahajan
E. Yeh
In this paper, cross-layer design of transmitting data packets over AWGN fading channel with adaptive decision feedback is considered. The t… (see more)ransmitter decides the number of packets to transmit and the threshold of the decision feedback based on the queue length and the channel state. The transmit power is chosen such that the probability of error is below a pre-specified threshold. We model the system as a Markov decision process and use ideas from lattice theory to establish qualitative properties of optimal transmission strategies. In particular, we show that: (i) if the channel state remains the same and the number of packets in the queue increase, then the optimal policy either transmits more packets or uses a smaller decision feedback threshold or both; and (ii) if the number of packets in the queue remain the same and the channel quality deteriorates, then the optimal policy either transmits fewer packets or uses a larger threshold for the decision feedback or both. We also show under rate constraints that if the channel gains for all channel states are above a threshold, then the “or” in the above characterization can be replaced by “and”. Finally, we present a numerical example showing that adaptive decision feedback significantly improves the power-delay trade-off as compared with the case of no feedback.
2019-12-31
WiOpt (published)
dblp.uni-trier.de