Publications

Generalization Limits of Graph Neural Networks in Identity Effects Learning
Giuseppe Alessio D'inverno
Simone Brugiapaglia
Mirco Ravanelli
Graph Neural Networks (GNNs) have emerged as a powerful tool for data-driven learning on various graph domains. They are usually based on a … (see more)message-passing mechanism and have gained increasing popularity for their intuitive formulation, which is closely linked to the Weisfeiler-Lehman (WL) test for graph isomorphism to which they have been proven equivalent in terms of expressive power. In this work, we establish new generalization properties and fundamental limits of GNNs in the context of learning so-called identity effects, i.e., the task of determining whether an object is composed of two identical components or not. Our study is motivated by the need to understand the capabilities of GNNs when performing simple cognitive tasks, with potential applications in computational linguistics and chemistry. We analyze two case studies: (i) two-letters words, for which we show that GNNs trained via stochastic gradient descent are unable to generalize to unseen letters when utilizing orthogonal encodings like one-hot representations; (ii) dicyclic graphs, i.e., graphs composed of two cycles, for which we present positive existence results leveraging the connection between GNNs and the WL test. Our theoretical analysis is supported by an extensive numerical study.
2025-01-01
Neural Networks (published)
doi.org
arxiv.org
Ex Post Conditions for the Exactness of Optimal Power Flow Conic Relaxations
Jean-Luc Lupien
Antoine Lesage-Landry
Convex relaxations of the optimal power flow (OPF) problem provide an efficient alternative to solving the intractable alternating current (… (see more)AC) optimal power flow. The conic subset of OPF convex relaxations, in particular, greatly accelerate resolution while leading to high-quality approximations that are exact in several scenarios. However, the sufficient conditions guaranteeing exactness are stringent, e.g., requiring radial topologies. In this short communication, we present two equivalent ex post conditions for the exactness of any conic relaxation of the OPF. These rely on obtaining either a rank-1 voltage matrix or self-coherent cycles. Instead of relying on sufficient conditions a priori, satisfying one of the presented ex post conditions acts as an exactness certificate for the computed solution. The operator can therefore obtain an optimality guarantee when solving a conic relaxation even when a priori exactness requirements are not met. Finally, we present numerical examples from the MATPOWER library where the ex post conditions hold even though the exactness sufficient conditions do not, thereby illustrating the use of the conditions.
2025-01-01
Electric Power Systems Research (published)
doi.org
arxiv.org
A stochastic integer programming approach to reserve staff scheduling with preferences
Carl Perreault‐Lafleur
Margarida Carvalho
Guy Desaulniers
2025-01-01
Int. Trans. Oper. Res. (published)
doi.org
arxiv.org
Towards Enhancing the Reproducibility of Deep Learning Bugs: An Empirical Study
Mehil B. Shah
Mohammad Masudur Rahman
Foutse Khomh
2025-01-01
Empir. Softw. Eng. (published)
doi.org
arxiv.org
Combining supervised learning and local search for the multicommodity capacitated fixed-charge network design problem
Charly Robinson La Rocca
Jean-François Cordeau
Emma Frejinger
The multicommodity capacitated fixed-charge network design problem has been extensively studied in the literature due to its wide range of a… (see more)pplications. Despite the fact that many sophisticated solution methods exist today, finding high-quality solutions to large-scale instances remains challenging. In this paper, we explore how a data-driven approach can help improve upon the state of the art. By leveraging machine learning models, we attempt to reveal patterns hidden in the data that might be difficult to capture with traditional optimization methods. For scalability, we propose a prediction method where the machine learning model is called at the level of each arc of the graph. We take advantage of off-the-shelf models trained via supervised learning to predict near-optimal solutions. Our experimental results include an algorithm design analysis that compares various integration strategies of predictions within local search algorithms. We benchmark the ML-based approach with respect to the state-of-the-art heuristic for this problem. The findings indicate that our method can outperform the leading heuristic on sets of instances sampled from a uniform distribution.
2024-12-01
Transportation Research Part E: Logistics and Transportation Review (published)
doi.org
arxiv.org
Caustics: A Python Package for Accelerated Strong Gravitational Lensing Simulations
Connor Stone
Alexandre Adam
Adam Coogan
M. J. Yantovski-Barth
Andreas Filipp
Landung Setiawan
Cordero Core
Ronan Legin
Charles Wilson
Gabriel Missael Barco
Yashar Hezaveh
Laurence Perreault-Levasseur
2024-11-22
Journal of Open Source Software (published)
doi.org
arxiv.org
UTG: Towards a Unified View of Snapshot and Event Based Models for Temporal Graphs
Shenyang Huang
Farimah Poursafaei
Reihaneh Rabbany
Guillaume Rabusseau
Emanuele Rossi
2024-11-16
logconference.io/LOG/2024/Conference (poster)
doi.org
openreview.net
EDAI Framework for Integrating Equity, Diversity, and Inclusion Throughout the Lifecycle of AI to Improve Health and Oral Health Care: Qualitative Study
Samira Abbasgholizadeh-Rahimi
Richa Shrivastava
Anita Brown-Johnson
Pascale Caidor
Claire Davies
Amal Idrissi Janati
Pascaline Kengne Talla
Sreenath Madathil
Bettina M Willie
Elham Emami
2024-11-15
Journal of Medical Internet Research (published)
doi.org
Outcomes of guidelines from health technology assessment organizations in community-based primary care: a systematic mixed studies review
Ashkan Baradaran
Raymond Tolentino
Roland Grad
Isabelle Ganache
Genevieve Gore
Samira Abbasgholizadeh-Rahimi
Pierre Pluye
2024-11-14
International Journal of Technology Assessment in Health Care (published)
doi.org
Abstract 4142894: Multimorbidity Trajectories Across the Lifespan in Patients with Congenital Heart Disease
Chao Li
Aihua Liu
Solomon Bendayan
Liming Guo
Judith Therrien
Archer Yang
Robyn Tamblyn
Jay Brophy
Yue Li
Ariane Marelli
Background: Befitted from advances in medical care, patients with congenital heart disease (CHD) now survive to adulthood but face elevated… (see more) risks of both cardiac and non-cardiac complications. Understanding the trajectories of comorbidity development over a patient's lifespan is cornerstone to optimize care expected to improve long-term health outcomes. Research Aim: This study aims to investigate the temporal sequences and evolution of comorbidities in CHD patients across their lifespan. We hypothesize that multimorbidity trajectories in CHD patients are linked to CHD lesion severity and age at onset of specific comorbidities. Methods: Using the Quebec CHD database which comprised data in outpatient visits, hospitalization records and vital status from 1983 to 2017, we designed a longitudinal cohort study evaluating the development of 39 comorbidities coded using ICD-9/10. Temporal sequences were mapped using median age of onset. Associations between disease pairs were quantified by hazard ratios from Cox proportional hazard models adjusting for age, sex, genetic syndrome, competing risks of death, and taking into account the time-varying nature of the predictor diseases. Results: The cohort included 9,764 individuals with severe and 127,729 with non-severe CHD lesions. In severe CHD patients, most comorbidities developed between ages 25 and 40. Comorbidity progression began with childhood cardiovascular diseases, followed by systemic diseases such as diabetes, liver and kidney diseases, and advanced to heart failure and dementia in middle adulthood. In addition, mental disorders emerged in early adulthood and were associated with subsequent development of kidney diseases and dementia. Different trajectories were observed in non-severe CHD patients with 2-3 decades later disease onsets and non-differential onsets between cardiovascular and systemic complications (Figure). Conclusions: Distinct multimorbidity trajectories were observed in CHD patients by CHD lesion severity. In patients with severe CHD lesions, early systemic diseases significantly influenced subsequent complications. These findings highlight the need for well-timed surveillance guidelines and interventions to improve health outcomes.
2024-11-12
Circulation (published)
doi.org
Beyond the Safety Bundle: Auditing the Helpful and Harmless Dataset
Khaoula Chehbouni
Jonathan Colacco-Carr
Yash More
Jackie Ck Cheung
Golnoosh Farnadi
In an effort to mitigate the harms of large language models (LLMs), learning from human feedback (LHF) has been used to steer LLMs towards o… (see more)utputs that are intended to be both less harmful and more helpful. Despite the widespread adoption of LHF in practice, the quality of this feedback and its effectiveness as a safety mitigation technique remain unclear. This study addresses these issues by auditing the widely-used Helpful and Harmless (HH) dataset by Anthropic. Our work includes: (1) a thorough investigation of the dataset's content through both manual and automated evaluation; (2) experiments demonstrating the dataset's impact on models' safety; and (3) an analysis of the 100 most influential papers citing this dataset. Through our audit, we showcase how conceptualization failures and quality issues identified in the HH dataset can create additional harms by leading to disparate safety behaviors across demographic groups. Our findings highlight the need for more nuanced, context-sensitive approaches to safety mitigation in LLMs.
2024-11-12
ArXiv (preprint)
arxiv.org
arxiv.org
Fault Localization in Deep Learning-based Software: A System-level Approach
Mohammad Mehdi Morovati
Amin Nikanjam
Foutse Khomh
2024-11-12
ArXiv (preprint)
arxiv.org
arxiv.org