Publications

Comment on Starke et al.: “Computing schizophrenia: ethical challenges for machine learning in psychiatry”: From machine learning to student learning: pedagogical challenges for psychiatry – Corrigendum

Christophe Gauld

Jean‐Arthur Micoulaud‐Franchi

Guillaume Dumas

2021-03-04

Psychological Medicine (publié)

doi.org

QBSUM: a Large-Scale Query-Based Document Summarization Dataset from Real-world Applications

Mingjun Zhao

Shengli Yan

Bang Liu

Xinwang Zhong

Qian Hao

Haolan Chen

Di Niu

Bo Long

Wei-dong Guo

2021-03-01

Computer Speech & Language (publié)

doi.org

arxiv.org

Towards robust and replicable sex differences in the intrinsic brain function of autism

Dorothea L. Floris

José O. A. Filho

Meng-Chuan Lai

Steve Giavasis

Marianne Oldehinkel

Maarten Mennes

Tony Charman

Julian Tillmann

Guillaume Dumas

Christine Ecker

Flavio Dell’Acqua

Tobias Banaschewski

Carolin Moessnang

Simon Baron-Cohen

Sarah Durston

Eva Loth

Declan Murphy

Jan K. Buitelaar

Christian Beckmann

Michael P. Milham … (voir 1 de plus)

Adriana Di Martino

2021-03-01

Molecular Autism (publié)

doi.org

From Generative Models to Generative Passages: A Computational Approach to (Neuro) Phenomenology

Maxwell J. D. Ramstead

Anil K. Seth

Casper Hesp

Lars Sandved-Smith

Jonas Mago

Michael Lifshitz

Giuseppe Pagnoni

Ryan Smith

Guillaume Dumas

Andrew E. Lutz

Antoine Lutz

Karl Friston

Axel Constant

2021-02-23

Review of Philosophy and Psychology (publié)

doi.org

Interacting brains revisited: A cross‐brain network neuroscience perspective

Christian Gerloff

Kerstin Konrad

Danilo Bzdok

Christina Büsing

Vanessa Reindl

Elucidating the neural basis of social behavior is a long-standing challenge in neuroscience. Such endeavors are driven by attempts to exten… (voir plus)d the isolated perspective on the human brain by considering interacting persons’ brain activities, but a theoretical and computational framework for this purpose is still in its infancy. Here, we posit a comprehensive framework based on bipartite graphs for interbrain networks and address whether they provide meaningful insights into the neural underpinnings of social interactions. First, we show that the nodal density of such graphs exhibits nonrandom properties. While the current analyses mostly rely on global metrics, we encode the regions’ roles via matrix decomposition to obtain an interpretable network representation yielding both global and local insights. With Bayesian modeling, we reveal how synchrony patterns seeded in specific brain regions contribute to global effects. Beyond inferential inquiries, we demonstrate that graph representations can be used to predict individual social characteristics, outperforming functional connectivity estimators for this purpose. In the future, this may provide a means of characterizing individual variations in social behavior or identifying biomarkers for social interaction and disorders.

2021-02-20

bioRxiv (preprint)

doi.org

Model-Invariant State Abstractions for Model-Based Reinforcement Learning

Manan Tomar

Amy Zhang

Roberto Calandra

Matthew E. Taylor

Joelle Pineau

Accuracy and generalization of dynamics models is key to the success of model-based reinforcement learning (MBRL). As the complexity of task… (voir plus)s increases, so does the sample inefficiency of learning accurate dynamics models. However, many complex tasks also exhibit sparsity in the dynamics, i.e., actions have only a local effect on the system dynamics. In this paper, we exploit this property with a causal invariance perspective in the single-task setting, introducing a new type of state abstraction called \textit{model-invariance}. Unlike previous forms of state abstractions, a model-invariance state abstraction leverages causal sparsity over state variables. This allows for compositional generalization to unseen states, something that non-factored forms of state abstractions cannot do. We prove that an optimal policy can be learned over this model-invariance state abstraction and show improved generalization in a simple toy domain. Next, we propose a practical method to approximately learn a model-invariant representation for complex domains and validate our approach by showing improved modelling performance over standard maximum likelihood approaches on challenging tasks, such as the MuJoCo-based Humanoid. Finally, within the MBRL setting we show strong performance gains with respect to sample efficiency across a host of other continuous control tasks.

2021-02-19

ArXiv (prépublication)

openreview.net

Concurrent prescriptions for opioids and benzodiazepines and risk of opioid overdose: protocol for a retrospective cohort study using linked administrative data

Erin Y Liu

Robyn Tamblyn

Kristian B Filion

David Buckeridge

2021-02-18

BMJ Open (publié)

doi.org

Smart Futures Based Resource Trading and Coalition Formation for Real-Time Mobile Data Processing

Ruitao Chen

Xianbin Wang

Xue (Steve) Liu

Collaboration among mobile devices (MDs) is becoming more important, as it could augment computing capacity at the network edge through peer… (voir plus)-to-peer service provisioning, and directly enhance real-time computational performance in smart Internet-of-Things applications. As an important aspect of collaboration mechanism, conventional resource trading (RT) among MDs relies on an onsite interaction process, i.e., price negotiation between service providers and requesters, which, however, inevitably incurs excessive latency and degrades RT efficiency. To overcome this challenge, this article adopts the concept of futures contract (FC) used in financial market, and proposes a smart futures for low latency RT. This new technique enables MDs to form trading coalitions and negotiate multilateral forward contracts applied to a collaboration term in the future. To maximize the benefits of self-interested MDs, the negotiation process of FC is modelled as a coalition formation game comprised of three components executed in an iterative manner, i.e., futures resource allocation, revenue sharing and payment allocation, and distributed decision-making of individual MD. Additionally, a FC enforcement scheme is implemented to efficiently manage the onsite resource sharing via recording resource balances of different task-types and MDs. Simulation results prove the superiority of smart futures in RT latency reduction and trading fairness provisioning.

2021-02-18

IEEE Transactions on Services Computing (publié)

doi.org

Smart Futures Based Resource Trading and Coalition Formation for Real-Time Mobile Data Processing

Ruitao Chen

Xianbin Wang

Xue (Steve) Liu

Collaboration among mobile devices (MDs) is becoming more important, as it could augment computing capacity at the network edge through peer… (voir plus)-to-peer service provisioning, and directly enhance real-time computational performance in smart Internet-of-Things applications. As an important aspect of collaboration mechanism, conventional resource trading (RT) among MDs relies on an onsite interaction process, i.e., price negotiation between service providers and requesters, which, however, inevitably incurs excessive latency and degrades RT efficiency. To overcome this challenge, this article adopts the concept of futures contract (FC) used in financial market, and proposes a smart futures for low latency RT. This new technique enables MDs to form trading coalitions and negotiate multilateral forward contracts applied to a collaboration term in the future. To maximize the benefits of self-interested MDs, the negotiation process of FC is modelled as a coalition formation game comprised of three components executed in an iterative manner, i.e., futures resource allocation, revenue sharing and payment allocation, and distributed decision-making of individual MD. Additionally, a FC enforcement scheme is implemented to efficiently manage the onsite resource sharing via recording resource balances of different task-types and MDs. Simulation results prove the superiority of smart futures in RT latency reduction and trading fairness provisioning.

2021-02-18

IEEE Transactions on Services Computing (published)

doi.org

SVRG meets AdaGrad: painless variance reduction

Benjamin Dubois-Taine

Sharan Vaswani

Reza Babanezhad Harikandeh

Mark Schmidt

Simon Lacoste-Julien

2021-02-18

ArXiv (preprint)

doi.org