Publications

Unified Models of Human Behavioral Agents in Bandits, Contextual Bandits and RL

Baihan Lin

Guillermo Cecchi

Djallel Bouneffouf

Jenna Reinen

Irina Rish

2020-05-09

ArXiv (preprint)

arxiv.org

Desirable features in a decision aid for prenatal screening – what do pregnant women and their partners think? A mixed methods pilot study

Titilayo Tatiana Agbadje

S. A. Rahimi

Mélissa Côté

Andrée-Anne Tremblay

Mariama Penda Diallo

Hélène Elidor

Alex Poulin Herron

Codjo Djignefa Djade

France Légaré

Background To help pregnant women and their partners make informed value-congruent decisions about Down syndrome prenatal screening, our te… (see more)am developed two successive versions of a decision aid (DAv2017 and DAv2014). We aimed to assess pregnant women and their partners’ perceptions of the usefulness of the two DAs for preparing for decision making, their relative acceptability and their most desirable features. Methods This is a mixed methods pilot study. We recruited participants of study (women and their partners) when consulting for prenatal care in three clinical sites in Quebec City. To be eligible, women had to: (a) be at least 18 years old; (b) be more than 16 weeks pregnant; or having given birth in the previous year and (c) be able to speak and write in French or English. Both women and partners were invited to give their informed consent. We collected quantitative data on the usefulness of the DAs for preparing for decision making and their relative acceptability. We developed an interview grid based on the Technology Acceptance Model and Acceptability questionnaire to explore their perceptions of the most desirable features. We performed descriptive statistics and deductive analysis. Results Overall, 23 couples and 16 individual women participated in the study. The majority of participants were between 25 and 34 years old (79% of women and 59% of partners) and highly educated (66.7% of women and 54% of partners had a university-level education). DAv2017 scored higher for usefulness for preparing for decision making (86.2 ± 13 out of 100 for DAv2017 and 77.7 ± 14 for DAv2014). For most dimensions, DAv2017 was more acceptable than DAv2014 (e.g. the amount of information was found “just right” by 80% of participants for DAv2017 against 56% for DAv2014). However, participants preferred the presentation and the values clarification exercise of DAv2014. In their opinion, neither DA presented information in a completely balanced manner. They suggested adding more information about raising Down syndrome children, replacing frequencies with percentages, different values clarification methods, and a section for the partner. Conclusions A new user-centered version of the prenatal screening DA will integrate participants’ suggestions to reflect end users’ priorities.

2020-05-07

(published)

doi.org

Leveraging cluster backbones for improving MAP inference in statistical relational models

Mohamed Hamza Ibrahim

Christopher Pal

Gilles Pesant

2020-05-06

Annals of Mathematics and Artificial Intelligence (published)

doi.org

Option-Critic in Cooperative Multi-Agent Systems

Jhelum Chakravorty

Nadeem Ward

Julien Roy

Maxime Chevalier-Boisvert

Sumana Basu

Andrei Lupu

Doina Precup

In this paper, we investigate learning temporal abstractions in cooperative multi-agent systems, using the options framework (Sutton et al, … (see more)1999). First, we address the planning problem for the decentralized POMDP represented by the multi-agent system, by introducing a \emph{common information approach}. We use the notion of \emph{common beliefs} and broadcasting to solve an equivalent centralized POMDP problem. Then, we propose the Distributed Option Critic (DOC) algorithm, which uses centralized option evaluation and decentralized intra-option improvement. We theoretically analyze the asymptotic convergence of DOC and build a new multi-agent environment to demonstrate its validity. Our experiments empirically show that DOC performs competitively against baselines and scales with the number of agents.

2020-05-04

International Joint Conference on Autonomous Agents and Multiagent Systems (published)

doi.org

arxiv.org

Multi-Task Self-Supervised Learning for Robust Speech Recognition

Mirco Ravanelli

Jianyuan Zhong

Santiago Pascual

Pawel Swietojanski

Joao Monteiro

Jan Trmal

Yoshua Bengio

Despite the growing interest in unsupervised learning, extracting meaningful knowledge from unlabelled audio remains an open challenge. To t… (see more)ake a step in this direction, we recently proposed a problem-agnostic speech encoder (PASE), that combines a convolutional encoder followed by multiple neural networks, called workers, tasked to solve self-supervised problems (i.e., ones that do not require manual annotations as ground truth). PASE was shown to capture relevant speech information, including speaker voice-print and phonemes. This paper proposes PASE+, an improved version of PASE for robust speech recognition in noisy and reverberant environments. To this end, we employ an online speech distortion module, that contaminates the input signals with a variety of random disturbances. We then propose a revised encoder that better learns short- and long-term speech dynamics with an efficient combination of recurrent and convolutional networks. Finally, we refine the set of workers used in self-supervision to encourage better cooperation. Results on TIMIT, DIRHA and CHiME-5 show that PASE+ significantly outperforms both the previous version of PASE as well as common acoustic features. Interestingly, PASE+ learns transferable representations suitable for highly mismatched acoustic conditions.

2020-05-03

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (published)

doi.org

arxiv.org

Suitable e-Health Solutions for Older Adults with Dementia or Mild Cognitive Impairment: Perceptions of Health and Social Care Providers in Quebec City

Marie-Pierre Gagnon

Mame Ndiaye

Mylène Boucher

Samantha Dequanter

Ronald Buyl

Ellen Gorus

Anne Bourbonnais

Anik Giguère

S. A. Rahimi

: e-Health solutions offer a potential to improve the quality of life and safety of older adults with dementia or mild cognitive impairment … (see more)(MCI). In making better decisions for using eHealth technologies, health professionals should be aware and well informed about existing tools. Recent research shows the lack of knowledge on these technologies for older adults with dementia. In Quebec, current market offer for these technologies is supply-based, and not need-based. This study is part of a larger project and aims to understand the perceptions and needs of health and social care providers regarding e-health technologies for older adults with dementia or MCI. One focus group was carried out with six health and social care professionals at the St-Sacrement Hospital in Quebec City, Canada. The focus group enquired about the use of Information and Communication Technology (ICT) with older adults with cognitive impairment. Relevant examples of ICTs were presented to assess their knowledge level. The discussion was tape-recorded and transcripts were coded using the Nvivo software. Results revealed that aside from fall safety technologies, there is a lack of knowledge about other e-Health technologies for this population. Respondents acknowledged the value of ICTs and were willing to recommend some of them. Economic reasons, blind trust on ICTs and lack of confidence in patients’ capacity to use the solutions were the major limitations identified.

2020-05-02

Proceedings of the 6th International Conference on Information and Communication Technologies for Ageing Well and e-Health (published)

doi.org

Bringing proportional recovery into proportion: Bayesian modelling of post-stroke motor impairment

Anna K Bonkhoff

Thomas Hope

Danilo Bzdok

Adrian G Guggisberg

Rachel L Hawe

Sean P Dukelow

Anne K Rehme

Gereon R Fink

Christian Grefkes

Howard Bowman

Accurate predictions of motor impairment after stroke are of cardinal importance for the patient, clinician, and healthcare system. More tha… (see more)n 10 years ago, the proportional recovery rule was introduced by promising that high-fidelity predictions of recovery following stroke were based only on the initially lost motor function, at least for a specific fraction of patients. However, emerging evidence suggests that this recovery rule is subject to various confounds and may apply less universally than previously assumed. Here, we systematically revisited stroke outcome predictions by applying strategies to avoid confounds and fitting hierarchical Bayesian models. We jointly analysed 385 post-stroke trajectories from six separate studies—one of the largest overall datasets of upper limb motor recovery. We addressed confounding ceiling effects by introducing a subset approach and ensured correct model estimation through synthetic data simulations. Subsequently, we used model comparisons to assess the underlying nature of recovery within our empirical recovery data. The first model comparison, relying on the conventional fraction of patients called ‘fitters’, pointed to a combination of proportional to lost function and constant recovery. ‘Proportional to lost’ here describes the original notion of proportionality, indicating greater recovery in case of a more severe initial impairment. This combination explained only 32% of the variance in recovery, which is in stark contrast to previous reports of >80%. When instead analysing the complete spectrum of subjects, ‘fitters’ and ‘non-fitters’, a combination of proportional to spared function and constant recovery was favoured, implying a more significant improvement in case of more preserved function. Explained variance was at 53%. Therefore, our quantitative findings suggest that motor recovery post-stroke may exhibit some characteristics of proportionality. However, the variance explained was substantially reduced compared to what has previously been reported. This finding motivates future research moving beyond solely behaviour scores to explain stroke recovery and establish robust and discriminating single-subject predictions.

2020-04-30

Brain (published)

doi.org

General Principles of Gene Dosage Effects on Brain Structure

Claudia Modenato

Kuldeep Kumar

Clara A. Moreau

Catherine Schramm

Guillaume Huguet

Sandra Martin-Brevet

Aurélie Pain

Anne M. Maillard

Sonia Richetin

Borja Rodriguez-Herreros

Lester Melie-Garcia

Ana Dos Santos Silva

Marianne B.M. van den Bree

David E.J. Linden

Carrie E. Bearden

Danilo Bzdok

Sarah Lippé

Mallar Chakravarty

Bogdan Draganski

Sébastien Jacquemont

2020-04-30

Biological Psychiatry (published)

doi.org

HipoRank: Incorporating Hierarchical and Positional Information into Graph-based Unsupervised Long Document Extractive Summarization

Yue Dong

Andrei Mircea

Jackie CK Cheung

We propose a novel graph-based ranking model for unsupervised extractive summarization of long documents. Graph-based ranking models typical… (see more)ly represent documents as undirected fully-connected graphs, where a node is a sentence, an edge is weighted based on sentence-pair similarity, and sentence importance is measured via node centrality. Our method leverages positional and hierarchical information grounded in discourse structure to augment a document's graph representation with hierarchy and directionality. Experimental results on PubMed and arXiv datasets show that our approach outperforms strong unsupervised baselines by wide margins and performs comparably to some of the state-of-the-art supervised models that are trained on hundreds of thousands of examples. In addition, we find that our method provides comparable improvements with various distributional sentence representations; including BERT and RoBERTa models fine-tuned on sentence similarity.

2020-04-30

ArXiv (preprint)

arxiv.org