Publications

A modified Thompson sampling-based learning algorithm for unknown linear systems
Yi. Ouyang
Mukul Gagrani
Rahul Jain
We revisit the Thompson sampling-based learning algorithm for controlling an unknown linear system with quadratic cost proposed in [1]. This… (voir plus) algorithm operates in episodes of dynamic length and it is shown to have a regret bound of
2021-08-19
ArXiv (preprint)
doi.org
arxiv.org
Toward Optimal Solution for the Context-Attentive Bandit Problem
Djallel Bouneffouf
Raphael Feraud
Sohini Upadhyay
Irina Rish
Yasaman Khazaeni
2021-08-19
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (publié)
doi.org
Scalable Regret for Learning to Control Network-Coupled Subsystems With Unknown Dynamics
Sagar Sudhakara
Aditya Mahajan
Ashutosh Nayyar
Yi. Ouyang
In this article, we consider the problem of controlling an unknown linear quadratic Gaussian (LQG) system consisting of multiple subsystems … (voir plus)connected over a network. Our goal is to minimize and quantify the regret (i.e., loss in performance) of our learning and control strategy with respect to an oracle who knows the system model. Upfront viewing the interconnected subsystems globally and directly using existing LQG learning algorithms for the global system results in a regret that increases super-linearly with the number of subsystems. Instead, we propose a new Thompson sampling-based learning algorithm which exploits the structure of the underlying network. We show that the expected regret of the proposed algorithm is bounded by
2021-08-18
ArXiv (preprint)
doi.org
arxiv.org
Forgetting Enhances Episodic Control With Structured Memories
Annik Yalnizyan-Carson
Blake Richards
Forgetting is a normal process in healthy brains, and evidence suggests that the mammalian brain forgets more than is required based on limi… (voir plus)tations of mnemonic capacity. Episodic memories, in particular, are liable to be forgotten over time. Researchers have hypothesized that it may be beneficial for decision making to forget episodic memories over time. Reinforcement learning offers a normative framework in which to test such hypotheses. Here, we show that a reinforcement learning agent that uses an episodic memory cache to find rewards in maze environments can forget a large percentage of older memories without any performance impairments, if they utilize mnemonic representations that contain structural information about space. Moreover, we show that some forgetting can actually provide a benefit in performance compared to agents with unbounded memories. Our analyses of the agents show that forgetting reduces the influence of outdated information and states which are not frequently visited on the policies produced by the episodic control system. These results support the hypothesis that some degree of forgetting can be beneficial for decision making, which can help to explain why the brain forgets more than is required by capacity limitations.
2021-08-12
bioRxiv (preprint)
doi.org
Hybrid Harmony: A Multi-Person Neurofeedback Application for Interpersonal Synchrony
Phoebe Chen
Sophie Hendrikse
Kaia Sargent
Michele Romani
Matthias Oostrik
Tom F. Wilderjans
Sander Koole
Guillaume Dumas
David Medine
Suzanne Dikker
Recent years have seen a dramatic increase in studies measuring brain activity, physiological responses, and/or movement data from multiple … (voir plus)individuals during social interaction. For example, so-called “hyperscanning” research has demonstrated that brain activity may become synchronized across people as a function of a range of factors. Such findings not only underscore the potential of hyperscanning techniques to capture meaningful aspects of naturalistic interactions, but also raise the possibility that hyperscanning can be leveraged as a tool to help improve such naturalistic interactions. Building on our previous work showing that exposing dyads to real-time inter-brain synchrony neurofeedback may help boost their interpersonal connectedness, we describe the biofeedback application Hybrid Harmony, a Brain-Computer Interface (BCI) that supports the simultaneous recording of multiple neurophysiological datastreams and the real-time visualization and sonification of inter-subject synchrony. We report results from 236 dyads experiencing synchrony neurofeedback during naturalistic face-to-face interactions, and show that pairs' social closeness and affective personality traits can be reliably captured with the inter-brain synchrony neurofeedback protocol, which incorporates several different online inter-subject connectivity analyses that can be applied interchangeably. Hybrid Harmony can be used by researchers who wish to study the effects of synchrony biofeedback, and by biofeedback artists and serious game developers who wish to incorporate multiplayer situations into their practice.
2021-08-12
Frontiers in Neuroergonomics (publié)
doi.org
Effectiveness of regional diffusion MRI measures in distinguishing multiple sclerosis abnormalities within the cervical spinal cord
Haykel Snoussi
Julien Cohen-Adad
Olivier Commowick
Benoit Combes
Elise Bannier
Slimane Tounekti
Anne Sophie Kerbrat
Christian Barillot
Emmanuel Caruyer
Multiple sclerosis (MS) is an inflammatory disorder of the central nervous system. Although conventional magnetic resonance imaging (MRI) is… (voir plus) widely used for MS diagnosis and clinical follow‐up, quantitative MRI has the potential to provide valuable intrinsic values of tissue properties that can enhance accuracy. In this study, we investigate the efficacy of diffusion MRI in distinguishing MS lesions within the cervical spinal cord, using a combination of metrics extracted from diffusion tensor imaging and Ball‐and‐Stick models.
2021-08-09
ArXiv (preprint)
doi.org
arxiv.org
Learning function from structure in neuromorphic networks
Laura E. Suárez
Blake Richards
Guillaume Lajoie
Bratislav Mišić
2021-08-09
Nature Machine Intelligence (publié)
doi.org
Neocortical inhibitory interneuron subtypes are differentially attuned to synchrony- and rate-coded information
Luke Y. Prince
Matthew M. Tran
Dorian Grey
Lydia Saad
Helen Chasiotis
Jeehyun Kwag
Michael M Kohl
Blake Richards
2021-08-05
Communications Biology (publié)
doi.org
Barriers and facilitators to patient engagement in patient safety from patients and healthcare professionals' perspectives: A systematic review and meta-synthesis.
Zahra Chegini
Morteza Arab‐Zozani
Sheikh Mohammed Shariful Islam
Georgia Tobiano
Samira Abbasgholizadeh-Rahimi
AIMS To explore patients' and healthcare professionals' (HCPs) perceived barriers and facilitators to patient engagement in patient safety. … (voir plus) METHODS We conducted a systematic review and meta-synthesis from five computerized databases, including PubMed/MEDLINE, Embase, Web of Science, Scopus and PsycINFO, as well as grey literature and reference lists of included studies. Data were last searched in December 2019 with no limitation on the year of publication. Qualitative and Mix-methods studies that explored HCPs' and patients' perceptions of barriers and facilitators to patient engagement in patient safety were included. Two authors independently screened the titles and the abstracts of studies. Next, the full texts of the screened studies were reviewed by two authors. Potential discrepancies were resolved by consensus with a third author. The Mixed Methods Appraisal Tool was used for quality appraisal. Thematic analysis was used to synthesize results. RESULTS Nineteen studies out of 2616 were included in this systematic review. Themes related to barriers included: patient unwillingness, HCPs' unwillingness, and inadequate infrastructures. Themes related to facilitators were: encouraging patients, sharing information with patients, establishing trustful relationship, establishing patient-centred care and improving organizational resources. CONCLUSION Patients have an active role in improving their safety. Strategies are required to address barriers that hinder or prevent patient engagement and create capacity and facilitate action.
2021-08-02
Nursing Forum (publié)
doi.org
Sequoia: A Software Framework to Unify Continual Learning Research
Fabrice Normandin
Florian Golemo
Oleksiy Ostapenko
Pau Rodriguez
Matthew D Riemer
J. Hurtado
Lucas Cecchi
Khimya Khetarpal
Dominic Zhao
Ryan Lindeborg
Timothee LESORT
David Vazquez
Laurent Charlin
Irina Rish
Massimo Caccia
The field of Continual Learning (CL) seeks to develop algorithms that accumulate knowledge and skills over time through interaction with non… (voir plus)-stationary environments. In practice, a plethora of evaluation procedures (settings) and algorithmic solutions (methods) exist, each with their own potentially disjoint set of assumptions. This variety makes measuring progress in CL difficult. We propose a taxonomy of settings, where each setting is described as a set of assumptions. A tree-shaped hierarchy emerges from this view, where more general settings become the parents of those with more restrictive assumptions. This makes it possible to use inheritance to share and reuse research, as developing a method for a given setting also makes it directly applicable onto any of its children. We instantiate this idea as a publicly available software framework called Sequoia, which features a wide variety of settings from both the Continual Supervised Learning (CSL) and Continual Reinforcement Learning (CRL) domains. Sequoia also includes a growing suite of methods which are easy to extend and customize, in addition to more specialized methods from external libraries. We hope that this new paradigm and its first implementation can help unify and accelerate research in CL. You can help us grow the tree by visiting (this GitHub URL).
2021-08-02
ArXiv (prépublication)
openreview.net
openreview.net
Guiding the Growth: Difficulty-Controllable Question Generation through Step-by-Step Rewriting
Yi Cheng
Siyao Li
Bang Liu
Ruihui Zhao
Sujian Li
Chenhua Lin
Yefeng Zheng
This paper explores the task of Difficulty-Controllable Question Generation (DCQG), which aims at generating questions with required difficu… (voir plus)lty levels. Previous research on this task mainly defines the difficulty of a question as whether it can be correctly answered by a Question Answering (QA) system, lacking interpretability and controllability. In our work, we redefine question difficulty as the number of inference steps required to answer it and argue that Question Generation (QG) systems should have stronger control over the logic of generated questions. To this end, we propose a novel framework that progressively increases question difficulty through step-by-step rewriting under the guidance of an extracted reasoning chain. A dataset is automatically constructed to facilitate the research, on which extensive experiments are conducted to test the performance of our method.
2021-08-01
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (publié)
doi.org
arxiv.org
Integrating Semantics and Neighborhood Information with Graph-Driven Generative Models for Document Retrieval
Zijing Ou
Qinliang Su
Jianxing Yu
Bang Liu
Jingwen Wang
Ruihui Zhao
Changyou Chen
Yefeng Zheng
With the need of fast retrieval speed and small memory footprint, document hashing has been playing a crucial role in large-scale informatio… (voir plus)n retrieval. To generate high-quality hashing code, both semantics and neighborhood information are crucial. However, most existing methods leverage only one of them or simply combine them via some intuitive criteria, lacking a theoretical principle to guide the integration process. In this paper, we encode the neighborhood information with a graph-induced Gaussian distribution, and propose to integrate the two types of information with a graph-driven generative model. To deal with the complicated correlations among documents, we further propose a tree-structured approximation method for learning. Under the approximation, we prove that the training objective can be decomposed into terms involving only singleton or pairwise documents, enabling the model to be trained as efficiently as uncorrelated ones. Extensive experimental results on three benchmark datasets show that our method achieves superior performance over state-of-the-art methods, demonstrating the effectiveness of the proposed model for simultaneously preserving semantic and neighborhood information.
2021-08-01
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (publié)
doi.org
arxiv.org