Martin Weiss

COVI-AgentSim: an Agent-based Model for Evaluating Methods of Digital Contact Tracing

Prateek Gupta

Tegan Maharaj

Martin Weiss

Nasim Rahaman

Hannah Alsdurf

Abhinav Sharma

Nanor Minoyan

Soren Harnois Leblanc

Victor Schmidt

Pierre-Luc St. Charles

Akshay Patel

David Buckeridge … (voir 9 de plus)

Joumana Ghosn

Yang Zhang

Bernhard Schölkopf

Jian Tang

Irina Rish

Christopher Pal

Joanna Merckx

Eilif B. Muller

Yoshua Bengio

The rapid global spread of COVID-19 has led to an unprecedented demand for effective methods to mitigate the spread of the disease, and vari… (voir plus)ous digital contact tracing (DCT) methods have emerged as a component of the solution. In order to make informed public health choices, there is a need for tools which allow evaluation and comparison of DCT methods. We introduce an agent-based compartmental simulator we call COVI-AgentSim, integrating detailed consideration of virology, disease progression, social contact networks, and mobility patterns, based on parameters derived from empirical research. We verify by comparing to real data that COVI-AgentSim is able to reproduce realistic COVID-19 spread dynamics, and perform a sensitivity analysis to verify that the relative performance of contact tracing methods are consistent across a range of settings. We use COVI-AgentSim to perform cost-benefit analyses comparing no DCT to: 1) standard binary contact tracing (BCT) that assigns binary recommendations based on binary test results; and 2) a rule-based method for feature-based contact tracing (FCT) that assigns a graded level of recommendation based on diverse individual features. We find all DCT methods consistently reduce the spread of the disease, and that the advantage of FCT over BCT is maintained over a wide range of adoption rates. Feature-based methods of contact tracing avert more disability-adjusted life years (DALYs) per socioeconomic cost (measured by productive hours lost). Our results suggest any DCT method can help save lives, support re-opening of economies, and prevent second-wave outbreaks, and that FCT methods are a promising direction for enriching BCT using self-reported symptoms, yielding earlier warning signals and a significantly reduced spread of the virus per socioeconomic cost.

2020-10-01

OpenReview.net/Anonymous_Preprint (inconnu)

doi.org

openreview.net

COVI White Paper - Version 1.1

Hannah Alsdurf

Edmond Belliveau

Yoshua Bengio

Tristan Deleu

Prateek Gupta

Daphne Ippolito

Richard Janda

Max Jarvie

Tyler Kolody

Sekoul Krastev

Tegan Maharaj

Robert Obryk

Dan Pilat

Valérie Pisano

Benjamin Prud’homme

Meng Qu

Nasim Rahaman

Irina Rish

Jean-François Rousseau

Victor Schmidt … (voir 5 de plus)

Abhinav Sharma

Brooke Struck

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… (voir plus)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.

2020-05-17

arXiv (prépublication)

doi.org

arxiv.org

Navigation Agents for the Visually Impaired: A Sidewalk Simulator and Experiments

Martin Weiss

Simon Chamorro

Roger Girgis

Margaux Luck

Samira E. Kahou

Joseph P. Cohen

Millions of blind and visually-impaired (BVI) people navigate urban environments every day, using smartphones for high-level path-planning a… (voir plus)nd white canes or guide dogs for local information. However, many BVI people still struggle to travel to new places. In our endeavor to create a navigation assistant for the BVI, we found that existing Reinforcement Learning (RL) environments were unsuitable for the task. This work introduces SEVN, a sidewalk simulation environment and a neural network-based approach to creating a navigation agent. SEVN contains panoramic images with labels for house numbers, doors, and street name signs, and formulations for several navigation tasks. We study the performance of an RL algorithm (PPO) in this setting. Our policy model fuses multi-modal observations in the form of variable resolution images, visible text, and simulated GPS data to navigate to a goal door. We hope that this dataset, simulator, and experimental results will provide a foundation for further research into the creation of agents that can assist members of the BVI community with outdoor navigation.

2020-05-11

Proceedings of the Conference on Robot Learning (publié)

doi.org

proceedings.mlr.press

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 … (voir 3 de plus)

Jian Tang

Martin Weiss

Yun William Yu

2019-12-31

(publié)

www.semanticscholar.org

A Survey of Mobile Computing for the Visually Impaired

Martin Weiss

Margaux Luck

Roger Girgis

Christopher Pal

Joseph Paul Cohen

The number of visually impaired or blind (VIB) people in the world is estimated at several hundred million. Based on a series of interviews … (voir plus)with the VIB and developers of assistive technology, this paper provides a survey of machine-learning based mobile applications and identifies the most relevant applications. We discuss the functionality of these apps, how they align with the needs and requirements of the VIB users, and how they can be improved with techniques such as federated learning and model compression. As a result of this study we identify promising future directions of research in mobile perception, micro-navigation, and content-summarization.

2018-11-24

ArXiv (prépublication)

arxiv.org

Towards Gene Expression Convolutions using Gene Interaction Graphs

We study the challenges of applying deep learning to gene expression data. We find experimentally that there exists non-linear signal in the… (voir plus) data, however is it not discovered automatically given the noise and low numbers of samples used in most research. We discuss how gene interaction graphs (same pathway, protein-protein, co-expression, or research paper text association) can be used to impose a bias on a deep model similar to the spatial bias imposed by convolutions on an image. We explore the usage of Graph Convolutional Neural Networks coupled with dropout and gene embeddings to utilize the graph information. We find this approach provides an advantage for particular tasks in a low data regime but is very dependent on the quality of the graph used. We conclude that more work should be done in this direction. We design experiments that show why existing methods fail to capture signal that is present in the data when features are added which clearly isolates the problem that needs to be addressed.

2018-06-17

ArXiv (prépublication)

arxiv.org

Graph Priors for Deep Neural Networks

In this work we explore how gene-gene interaction graphs can be used as a prior for the representation of a model to construct features base… (voir plus)d on known interactions between genes. Most existing machine learning work on graphs focuses on building models when data is confined to a graph structure. In this work we focus on using the information from a graph to build better representations in our models. We use the percolate task, determining if a path exists across a grid for a set of node values, as a proxy for gene pathways. We create variants of the percolate task to explore where existing methods fail. We test the limits of existing methods in order to determine what can be improved when applying these methods to a real task. This leads us to propose new methods based on Graph Convolutional Networks (GCN) that use pooling and dropout to deal with noise in the graph prior.

2018-02-11

International Conference on Machine Learning (publié)

openreview.net

Publications du Fellowship en politiques de l'IA

La plateforme Mila Ventures

Boussole des politiques en IA

Martin Weiss

Publications

Publications du Fellowship en politiques de l'IA

La plateforme Mila Ventures

Boussole des politiques en IA

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Martin Weiss

Publications