Publications

Class imbalance should not throw you off balance: Choosing the right classifiers and performance metrics for brain decoding with imbalanced data
Philipp Thölke
Yorguin-Jose Mantilla-Ramos
Hamza Abdelhedi
Charlotte Maschke
Arthur Dehgan
Yann Harel
Anirudha Kemtur
Loubna Mekki Berrada
Myriam Sahraoui
Tammy Young
Antoine Bellemare Pépin
Clara El Khantour
Mathieu Landry
Annalisa Pascarella
Vanessa Hadid
Etienne Combrisson
Jordan O'Byrne
Karim Jerbi
Machine learning (ML) is increasingly used in cognitive, computational and clinical neuroscience. The reliable and efficient application of … (voir plus)ML requires a sound understanding of its subtleties and limitations. Training ML models on datasets with imbalanced classes is a particularly common problem, and it can have severe consequences if not adequately addressed. With the neuroscience ML user in mind, this paper provides a didactic assessment of the class imbalance problem and illustrates its impact through systematic manipulation of data imbalance ratios in (i) simulated data and (ii) brain data recorded with electroencephalography (EEG), magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI). Our results illustrate how the widely-used Accuracy (Acc) metric, which measures the overall proportion of successful predictions, yields misleadingly high performances, as class imbalance increases. Because Acc weights the per-class ratios of correct predictions proportionally to class size, it largely disregards the performance on the minority class. A binary classification model that learns to systematically vote for the majority class will yield an artificially high decoding accuracy that directly reflects the imbalance between the two classes, rather than any genuine generalizable ability to discriminate between them. We show that other evaluation metrics such as the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC), and the less common Balanced Accuracy (BAcc) metric - defined as the arithmetic mean between sensitivity and specificity, provide more reliable performance evaluations for imbalanced data. Our findings also highlight the robustness of Random Forest (RF), and the benefits of using stratified cross-validation and hyperprameter optimization to tackle data imbalance. Critically, for neuroscience ML applications that seek to minimize overall classification error, we recommend the routine use of BAcc, which in the specific case of balanced data is equivalent to using standard Acc, and readily extends to multi-class settings. Importantly, we present a list of recommendations for dealing with imbalanced data, as well as open-source code to allow the neuroscience community to replicate and extend our observations and explore alternative approaches to coping with imbalanced data.
2023-07-31
NeuroImage (publié)
doi.org
A cop-winning strategy on strongly cop-win graphs
Jos'ee Desharnais
Franccois Laviolette
Héli Marcoux
Norbert Polat
2023-07-31
Discrete Mathematics (publié)
doi.org
Decentralized Linear Quadratic Systems With Major and Minor Agents and Non-Gaussian Noise
Mohammad Afshari
Aditya Mahajan
A decentralized linear quadratic system with a major agent and a collection of minor agents is considered. The major agent affects the minor… (voir plus) agents, but not vice versa. The state of the major agent is observed by all agents. In addition, the minor agents have a noisy observation of their local state. The noise process is not assumed to be Gaussian. The structures of the optimal strategy and the best linear strategy are characterized. It is shown that the major agent's optimal control action is a linear function of the major agent's minimum mean-squared error (MMSE) estimate of the system state while the minor agent's optimal control action is a linear function of the major agent's MMSE estimate of the system state and a “correction term” that depends on the difference of the minor agent's MMSE estimate of its local state and the major agent's MMSE estimate of the minor agent's local state. Since the noise is non-Gaussian, the minor agent's MMSE estimate is a nonlinear function of its observation. It is shown that replacing the minor agent's MMSE estimate with its linear least mean square estimate gives the best linear control strategy. The results are proved using a direct method based on conditional independence, common-information-based splitting of state and control actions, and simplifying the per-step cost based on conditional independence, orthogonality principle, and completion of squares.
2023-07-31
IEEE Transactions on Automatic Control (publié)
doi.org
arxiv.org
Determinants of Access to Essential Surgery in the Democratic Republic of Congo
Luc Malemo Kalisya
Ava Yap
Boniface Mitume
Christian Salmon
Kambale Karafuli
Dan Poenaru
Rosebella Onyango
2023-07-31
Journal of Surgical Research (publié)
doi.org
Learning Neural Implicit Representations with Surface Signal Parameterizations
Yanran Guan
Andrei Chubarau
Ruby Rao
D. Nowrouzezahrai
2023-07-31
Computers & Graphics (publié)
doi.org
arxiv.org
Machine learning-assisted selection of adsorption-based carbon dioxide capture materials
Eslam G. Al-Sakkari
Ahmed Ragab
Terry M.Y. So
Marzieh Shokrollahi
Hanane Dagdougui
Philippe Navarri
Ali Elkamel
Mouloud Amazouz
2023-07-31
Journal of Environmental Chemical Engineering (publié)
doi.org
Neural efficiency in an aviation task with different levels of difficulty: Assessing different biometrics during a performance task
Mohammad Javad Darvishi Bayazi
Andrew Law
Sergio Mejia Romero
Sion Jennings
Irina Rish
Jocelyn Faubert
2023-07-31
Journal of Vision (publié)
doi.org
Neural representation of occluded objects in visual cortex
Courtney Mansfield
Tim Kietzmann
Jasper JF van den Bosch
Ian Charest
Marieke Mur
Nikolaus Kriegeskorte
Fraser Smith
2023-07-31
Journal of Vision (publié)
doi.org
Reconstructing mental images using Bubbles and electroencephalography
Audrey Lamy-Proulx
Jasper van den Bosch
Catherine Landry
Peter Brotherwood
Vincent Taschereau-Dumouchel
Frédéric Gosselin
Ian Charest
2023-07-31
Journal of Vision (publié)
doi.org
Selection for immune evasion in SARS-CoV-2 revealed by high-resolution epitope mapping and sequence analysis
Arnaud N’Guessan
Senthilkumar Kailasam
Fatima Mostefai
Raphaël Poujol
Jean-Christophe Grenier
Nailya Ismailova
Paola Contini
Raffaele De Palma
Carsten Haber
Volker Stadler
Guillaume Bourque
Julie G. Hussin
B. Jesse Shapiro
Jörg H. Fritz
Ciriaco A. Piccirillo
2023-07-31
iScience (publié)
doi.org
Single-cell multi-omics topic embedding reveals cell-type-specific and COVID-19 severity-related immune signatures
Manqi Zhou
Hao Zhang
Zilong Bai
Dylan Mann-Krzisnik
Fei Wang
Yue Li
2023-07-31
Cell Reports Methods (publié)
doi.org
The Different Faces of AI Ethics Across the World: A Principle-to-Practice Gap Analysis
Lionel Nganyewou Tidjon
Foutse Khomh
Artificial Intelligence (AI) is transforming our daily life with many applications in healthcare, space exploration, banking, and finance. T… (voir plus)his rapid progress in AI has brought increasing attention to the potential impacts of AI technologies on society, with ethically questionable consequences. In recent years, several ethical principles have been released by governments, national organizations, and international organizations. These principles outline high-level precepts to guide the ethical development, deployment, and governance of AI. However, the abstract nature, diversity, and context-dependence of these principles make them difficult to implement and operationalize, resulting in gaps between principles and their execution. Most recent work analyzed and summarized existing AI principles and guidelines but did not provide findings on principle-to-practice gaps nor how to mitigate them. These findings are particularly important to ensure that AI practical guidances are aligned with ethical principles and values. In this article, we provide a contextual and global evaluation of current ethical AI principles for all continents, with the aim to identify potential principle characteristics tailored to specific countries or applicable across countries. Next, we analyze the current level of AI readiness and current practical guidances of ethical AI principles in different countries, to identify gaps in the practical guidance of AI principles and their causes. Finally, we propose recommendations to mitigate the principle-to-practice gaps.
2023-07-31
IEEE Transactions on Artificial Intelligence (publié)
doi.org