Publications

All in This Together? A Preregistered Report on Deservingness of Government Aid During the COVID-19 Pandemic
Aengus Bridgman
Eric Merkley
Peter John Loewen
Taylor Owen
Derek Ruths
2021-03-30
Journal of Experimental Political Science (published)
doi.org
Loneliness and Neurocognitive Aging
R. Nathan Spreng
Danilo Bzdok
2021-03-28
Advances in Geriatric Medicine and Research (published)
doi.org
Mapping gene transcription and neurocognition across human neocortex
Justine Y. Hansen
Ross D. Markello
Jacob W. Vogel
Jakob Seidlitz
Danilo Bzdok
Bratislav Misic
2021-03-24
Nature Human Behaviour (published)
doi.org
Evaluating the Integration of One Health in Surveillance Systems for Antimicrobial Use and Resistance: A Conceptual Framework
Cécile Aenishaenslin
Barbara Häsler
André Ravel
E. Jane Parmley
Sarah Mediouni
Houda Bennani
Katharina D. C. Stärk
David L. Buckeridge
It is now widely acknowledged that surveillance of antimicrobial resistance (AMR) must adopt a “One Health” (OH) approach to successfull… (see more)y address the significant threats this global public health issue poses to humans, animals, and the environment. While many protocols exist for the evaluation of surveillance, the specific aspect of the integration of a OH approach into surveillance systems for AMR and antimicrobial Use (AMU), suffers from a lack of common and accepted guidelines and metrics for its monitoring and evaluation functions. This article presents a conceptual framework to evaluate the integration of OH in surveillance systems for AMR and AMU, named the Integrated Surveillance System Evaluation framework (ISSE framework). The ISSE framework aims to assist stakeholders and researchers who design an overall evaluation plan to select the relevant evaluation questions and tools. The framework was developed in partnership with the Canadian Integrated Program for Antimicrobial Resistance Surveillance (CIPARS). It consists of five evaluation components, which consider the capacity of the system to: [1] integrate a OH approach, [2] produce OH information and expertise, [3] generate actionable knowledge, [4] influence decision-making, and [5] positively impact outcomes. For each component, a set of evaluation questions is defined, and links to other available evaluation tools are shown. The ISSE framework helps evaluators to systematically assess the different OH aspects of a surveillance system, to gain comprehensive information on the performance and value of these integrated efforts, and to use the evaluation results to refine and improve the surveillance of AMR and AMU globally.
2021-03-23
Frontiers in Veterinary Science (published)
doi.org
Beyond Correlation versus Causation: Multi-brain Neuroscience Needs Explanation
Quentin Moreau
Guillaume Dumas
2021-03-18
Trends in Cognitive Sciences (published)
doi.org
Factors influencing consumer choice: a study of apparel and sustainable cues from Canadian and Indian consumers’ perspectives
Osmud Rahman
Benjamin C. M. Fung
Devender Kharb
2021-03-18
International Journal of Fashion Design, Technology and Education (published)
doi.org
Implicit Regularization via Neural Feature Alignment
Aristide Baratin
Thomas George
César Laurent
R Devon Hjelm
Guillaume Lajoie
Pascal Vincent
Simon Lacoste-Julien
We approach the problem of implicit regularization in deep learning from a geometrical viewpoint. We highlight a regularization effect induc… (see more)ed by a dynamical alignment of the neural tangent features introduced by Jacot et al, along a small number of task-relevant directions. This can be interpreted as a combined mechanism of feature selection and compression. By extrapolating a new analysis of Rademacher complexity bounds for linear models, we motivate and study a heuristic complexity measure that captures this phenomenon, in terms of sequences of tangent kernel classes along optimization paths.
2021-03-17
International Conference on Artificial Intelligence and Statistics (unknown)
doi.org
proceedings.mlr.press
Intention estimation and controllable behaviour models for traffic merges
Aditya Mahajan
Takayasu Kumano
Yuji Yasui
This work focuses on decision making for automated driving vehicles in interaction rich scenarios like traffic merges in a flexibly assertiv… (see more)e yet safe manner. We propose a Q-learning based approach, that takes in active intention inferences as additional inputs besides the directly observed state inputs. The outputs of Q-function are processed to select a decision by a modulation function, which can control how assertively or defensively the agent behaves.
2021-03-17
SICE Journal of Control Measurement and System Integration (published)
doi.org
Learning with Gradient Descent and Weakly Convex Losses
Dominic Richards
Michael G. Rabbat
We study the learning performance of gradient descent when the empirical risk is weakly convex, namely, the smallest negative eigenvalue of … (see more)the empirical risk's Hessian is bounded in magnitude. By showing that this eigenvalue can control the stability of gradient descent, generalisation error bounds are proven that hold under a wider range of step sizes compared to previous work. Out of sample guarantees are then achieved by decomposing the test error into generalisation, optimisation and approximation errors, each of which can be bounded and traded off with respect to algorithmic parameters, sample size and magnitude of this eigenvalue. In the case of a two layer neural network, we demonstrate that the empirical risk can satisfy a notion of local weak convexity, specifically, the Hessian's smallest eigenvalue during training can be controlled by the normalisation of the layers, i.e., network scaling. This allows test error guarantees to then be achieved when the population risk minimiser satisfies a complexity assumption. By trading off the network complexity and scaling, insights are gained into the implicit bias of neural network scaling, which are further supported by experimental findings.
2021-03-17
Proceedings of The 24th International Conference on Artificial Intelligence and Statistics (published)
proceedings.mlr.press
proceedings.mlr.press
Quantum Tensor Networks, Stochastic Processes, and Weighted Automata
Siddarth Srinivasan
Sandesh Adhikary
Jacob Miller
Guillaume Rabusseau
Byron Boots
Modeling joint probability distributions over sequences has been studied from many perspectives. The physics community developed matrix prod… (see more)uct states, a tensor-train decomposition for probabilistic modeling, motivated by the need to tractably model many-body systems. But similar models have also been studied in the stochastic processes and weighted automata literature, with little work on how these bodies of work relate to each other. We address this gap by showing how stationary or uniform versions of popular quantum tensor network models have equivalent representations in the stochastic processes and weighted automata literature, in the limit of infinitely long sequences. We demonstrate several equivalence results between models used in these three communities: (i) uniform variants of matrix product states, Born machines and locally purified states from the quantum tensor networks literature, (ii) predictive state representations, hidden Markov models, norm-observable operator models and hidden quantum Markov models from the stochastic process literature,and (iii) stochastic weighted automata, probabilistic automata and quadratic automata from the formal languages literature. Such connections may open the door for results and methods developed in one area to be applied in another.
2021-03-17
Proceedings of The 24th International Conference on Artificial Intelligence and Statistics (published)
doi.org
proceedings.mlr.press
Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence
Nicolas Loizou
Sharan Vaswani
Issam Laradji
Simon Lacoste-Julien
We propose a stochastic variant of the classical Polyak step-size (Polyak, 1987) commonly used in the subgradient method. Although computing… (see more) the Polyak step-size requires knowledge of the optimal function values, this information is readily available for typical modern machine learning applications. Consequently, the proposed stochastic Polyak step-size (SPS) is an attractive choice for setting the learning rate for stochastic gradient descent (SGD). We provide theoretical convergence guarantees for SGD equipped with SPS in different settings, including strongly convex, convex and non-convex functions. Furthermore, our analysis results in novel convergence guarantees for SGD with a constant step-size. We show that SPS is particularly effective when training over-parameterized models capable of interpolating the training data. In this setting, we prove that SPS enables SGD to converge to the true solution at a fast rate without requiring the knowledge of any problem-dependent constants or additional computational overhead. We experimentally validate our theoretical results via extensive experiments on synthetic and real datasets. We demonstrate the strong performance of SGD with SPS compared to state-of-the-art optimization methods when training over-parameterized models.
2021-03-17
Proceedings of The 24th International Conference on Artificial Intelligence and Statistics (published)
doi.org
proceedings.mlr.press
A Study of Condition Numbers for First-Order Optimization
Charles Guille-escuret
Baptiste Goujaud
Manuela Girotti
Ioannis Mitliagkas
The study of first-order optimization algorithms (FOA) typically starts with assumptions on the objective functions, most commonly smoothnes… (see more)s and strong convexity. These metrics are used to tune the hyperparameters of FOA. We introduce a class of perturbations quantified via a new norm, called *-norm. We show that adding a small perturbation to the objective function has an equivalently small impact on the behavior of any FOA, which suggests that it should have a minor impact on the tuning of the algorithm. However, we show that smoothness and strong convexity can be heavily impacted by arbitrarily small perturbations, leading to excessively conservative tunings and convergence issues. In view of these observations, we propose a notion of continuity of the metrics, which is essential for a robust tuning strategy. Since smoothness and strong convexity are not continuous, we propose a comprehensive study of existing alternative metrics which we prove to be continuous. We describe their mutual relations and provide their guaranteed convergence rates for the Gradient Descent algorithm accordingly tuned. Finally we discuss how our work impacts the theoretical understanding of FOA and their performances.
2021-03-17
Proceedings of The 24th International Conference on Artificial Intelligence and Statistics (published)
doi.org
proceedings.mlr.press