Publications

COVI White Paper-Version 1.1
Hannah Alsdurf
Tristan Deleu
Prateek Gupta
Daphne Ippolito
Richard Janda
Max Jarvie
Tyler J. Kolody
Sekoul Krastev
Robert Obryk
Dan Pilat
Valerie Pisano
Benjamin Prud'homme
Meng Qu
Nasim Rahaman
Jean-franois Rousseau
abhinav sharma
Brooke Struck … (see 3 more)
Martin Weiss
Yun William Yu
The SARS-CoV-2 (Covid-19) pandemic has resulted in significant strain on health care and public health institutions around the world. Contac… (see more)t tracing is an essential tool for public health officials and local communities to change the course of the Covid-19 pandemic. Standard manual contact tracing of people infected with Covid-19, while the current gold standard, 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 applications has the potential to shift the paradigm of Covid-19 community spread. Although some countries have deployed centralized tracking systems through either GPS or Bluetooth, more privacy-protecting decentralized systems offer much of the same benefit without concentrating data in the hands of a state authority or in for-profit corporations. Additionally, machine learning methods can be used to circumvent some of the limitations of standard digital tracing by incorporating many clues (including medical conditions, self-reported symptoms, and numerous encounters with people at different risk levels, for different durations and distances) and their uncertainty into a more graded and precise estimation of infection and contagion risk. The estimated risk can be used to provide early risk awareness, personalized recommendations and relevant information to the user and connect them to health services. Finally, the non-identifying data about these risks can inform detailed epidemiological models trained jointly with the machine learning predictor, and these models can provide statistical evidence for the interaction and importance of different factors involved in the transmission of the disease. They can also be used to monitor, evaluate and optimize different health policy and confinement/deconfinement 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. Addendum 2020-07-14: The government of Canada has declined to endorse COVI and will be promoting a different app for decentralized contact tracing. In the interest of preventing fragmentation of the app landscape, COVI will therefore not be deployed to end users. We are currently still in the process of finalizing the project, and plan to release our code and models for academic consumption and to make them accessible to other States should they wish to deploy an app based on or inspired by said code and models. University of Ottawa, Mila, Université de Montréal, The Alan Turing Institute, University of Oxford, University of Pennsylvania, McGill University, Borden Ladner Gervais LLP, The Decision Lab, HEC Montréal, Max Planck Institute, Libéo, University of Toronto. Corresponding author general: richard.janda@mcgill.ca Corresponding author for public health: abhinav.sharma@mcgill.ca Corresponding author for privacy: ywyu@math.toronto.edu Corresponding author for machine learning: yoshua.bengio@mila.quebec Corresponding author for user perspective: brooke@thedecisionlab.com Corresponding author for technical implementation: jean-francois.rousseau@libeo.com 1 ar X iv :2 00 5. 08 50 2v 2 [ cs .C R ] 2 7 Ju l 2 02 0
Cross-layer communication over fading channels with adaptive decision feedback
Borna Sayedana
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.
Differentiable Causal Discovery from Interventional Data
Philippe Brouillard
Sébastien Lachapelle
Alexandre Lacoste
Discovering causal relationships in data is a challenging task that involves solving a combinatorial problem for which the solution is not a… (see more)lways identifiable. A new line of work reformulates the combinatorial problem as a continuous constrained optimization one, enabling the use of different powerful optimization techniques. However, methods based on this idea do not yet make use of interventional data, which can significantly alleviate identifiability issues. In this work, we propose a neural network-based method for this task that can leverage interventional data. We illustrate the flexibility of the continuous-constrained framework by taking advantage of expressive neural architectures such as normalizing flows. We show that our approach compares favorably to the state of the art in a variety of settings, including perfect and imperfect interventions for which the targeted nodes may even be unknown.
A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms
Divergent protein-coding genes and brain size in primates
Malesys Bourgeron Dumas
Simon Malesys
Thomas Bourgeron
17 The human brain differs from that of other primates, but the genetic basis of these differences 18 remains unclear. We investigated the e… (see more)volutionary pressures acting on almost all human 19 protein-coding genes ( N =11,667; 1:1 orthologs in primates) on the basis of their divergence 20 from those of early hominins, such as Neanderthals, and non-human primates. We confirm 21 that genes encoding brain-related proteins are among the most strongly conserved protein- 22 coding genes in the human genome. Combining our evolutionary pressure metrics for the 23 protein-coding genome with recent datasets, we found that this conservation applied to genes 24 functionally associated with the synapse and expressed in brain structures such as the 25 prefrontal cortex and the cerebellum. Conversely, several of the protein-coding genes that 26 diverge most in hominins relative to other primates are associated with brain-associated 27 diseases, such as micro/macrocephaly, dyslexia, and autism. We also showed that cerebellum 28 granule neurons express a set of divergent protein-coding genes that may have contributed to 29 the emergence of fine motor skills and social cognition in humans. This resource is available 30 from http://neanderthal.pasteur.fr and can be used to estimate evolutionary constraints acting 31 on a set of genes and to explore their relative contributions to human traits. 32
Electric Vehicles Equilibrium Model that Considers Queue Delay and Mixed Traffic
Nurit Oliker
Miguel F. Anjos
Bernard Gendron
This study develops an equilibrium model for electric vehicles (EVs) that considers both queue delays in charging stations and flow dependen… (see more)t travel times. This is a user equilibrium model that accounts for travel, charging and queuing time in the path choice modelling of EVs and the complementary traffic. Waiting and service times in charging stations are represented by an m/m/k queuing system. The model considers multiple vehicle and driver classes, expressing different battery capacity, initial charge state and range anxiety level. Feasible paths are found for multiple classes given their limited travel range. A numerical application exemplifies the limitations of EVs assignment and their impact on flow distribution.
Expressiveness and Learning of Hidden Quantum Markov Models
Sandesh M. Adhikary
Siddarth Srinivasan
Byron Boots
Extending classical probabilistic reasoning using the quantum mechanical view of probability has been of recent interest, particularly in th… (see more)e development of hidden quantum Markov models (HQMMs) to model stochastic processes. However, there has been little progress in characterizing the expressiveness of such models and learning them from data. We tackle these problems by showing that HQMMs are a special subclass of the general class of observable operator models (OOMs) that do not suffer from the \emph{negative probability problem} by design. We also provide a feasible retraction-based learning algorithm for HQMMs using constrained gradient descent on the Stiefel manifold of model parameters. We demonstrate that this approach is faster and scales to larger models than previous learning algorithms.
Fairness in Kidney Exchange Programs through Optimal Solutions Enumeration
Not all patients who need kidney transplant can find a donor with compatible characteristics. Kidney exchange programs (KEPs) seek to match … (see more)such incompatible patient-donor pairs together, usually with the objective of maximizing the total number of transplants. We propose a randomized policy for selecting an optimal solution in which patients’ equity of opportunity to receive a transplant is promoted. Our approach gives rise to the problem of enumerating all optimal solutions, which we tackle using a hybrid of constraint programming and linear programming. We empirically demonstrate the advantages of our proposed method over the common practice of using the first optimal solution obtained by a solver.
Fast and Furious Convergence: Stochastic Second Order Methods under Interpolation
Si Yi Meng
Sharan Vaswani
Issam Hadj Laradji
Mark Schmidt
We consider stochastic second-order methods for minimizing smooth and strongly-convex functions under an interpolation condition satisfied b… (see more)y over-parameterized models. Under this condition, we show that the regularized subsampled Newton method (R-SSN) achieves global linear convergence with an adaptive step-size and a constant batch-size. By growing the batch size for both the subsampled gradient and Hessian, we show that R-SSN can converge at a quadratic rate in a local neighbourhood of the solution. We also show that R-SSN attains local linear convergence for the family of self-concordant functions. Furthermore, we analyze stochastic BFGS algorithms in the interpolation setting and prove their global linear convergence. We empirically evaluate stochastic L-BFGS and a "Hessian-free" implementation of R-SSN for binary classification on synthetic, linearly-separable datasets and real datasets under a kernel mapping. Our experimental results demonstrate the fast convergence of these methods, both in terms of the number of iterations and wall-clock time.
GAIT: A Geometric Approach to Information Theory
Jose Gallego-Posada
Ankit Vani
Max Schwarzer
We advocate the use of a notion of entropy that reflects the relative abundances of the symbols in an alphabet, as well as the similarities … (see more)between them. This concept was originally introduced in theoretical ecology to study the diversity of ecosystems. Based on this notion of entropy, we introduce geometry-aware counterparts for several concepts and theorems in information theory. Notably, our proposed divergence exhibits performance on par with state-of-the-art methods based on the Wasserstein distance, but enjoys a closed-form expression that can be computed efficiently. We demonstrate the versatility of our method via experiments on a broad range of domains: training generative models, computing image barycenters, approximating empirical measures and counting modes.
How to make your optimizer generalize better
Sharan Vaswani
Reza Babenzhad
Sait AI Lab
Montreal.
Jose Gallego
Aaron Mishkin
We study the implicit regularization of optimization methods for linear models interpolating the training data in the under-parametrized and… (see more) over-parametrized regimes. For over-parameterized linear regression, where there are infinitely many interpolating solutions, different optimization methods can converge to solutions with varying generalization performance. In this setting, we show that projections onto linear spans can be used to move between solutions. Furthermore, via a simple reparameterization, we can ensure that an arbitrary optimizer converges to the minimum (cid:96) 2 -norm solution with favourable generalization properties. For under-parameterized linear clas-sification, optimizers can converge to different decision boundaries separating the data. We prove that for any such classifier, there exists a family of quadratic norms (cid:107)·(cid:107) P such that the classifier’s direction is the same as that of the maximum P -margin solution. We argue that analyzing convergence to the standard maximum (cid:96) 2 -margin is arbitrary and show that minimizing the norm induced by the data can result in better generalization. We validate our theoretical results via experiments on synthetic and real datasets.
Intelligent Tools for Precision Public Health.
Anya Okhmatovskaia