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William St-Arnaud

Doctorat - UdeM
Superviseur⋅e principal⋅e
Golnoosh Farnadi
Co-supervisor
Margarida Carvalho
Sujets de recherche
Apprentissage par renforcement
Apprentissage sur graphes
Modèles probabilistes
Systèmes dynamiques

Publications

Understanding the role of depth in the neural tangent kernel for overparameterized neural networks
William St-Arnaud
Margarida Carvalho
Golnoosh Farnadi
2025-10-31
arXiv (publié)
doi.org
arxiv.org
Adaptation, Comparison and Practical Implementation of Fairness Schemes in Kidney Exchange Programs
William St-Arnaud
Margarida Carvalho
Golnoosh Farnadi
In Kidney Exchange Programs (KEPs), each participating patient is registered together with an incompatible donor. Donors without an incompat… (voir plus)ible patient can also register. Then, KEPs typically maximize overall patient benefit through donor exchanges. This aggregation of benefits calls into question potential individual patient disparities in terms of access to transplantation in KEPs. Considering solely this utilitarian objective may become an issue in the case where multiple exchange plans are optimal or near-optimal. In fact, current KEP policies are all-or-nothing, meaning that only one exchange plan is determined. Each patient is either selected or not as part of that unique solution. In this work, we seek instead to find a policy that contemplates the probability of patients of being in a solution. To guide the determination of our policy, we adapt popular fairness schemes to KEPs to balance the usual approach of maximizing the utilitarian objective. Different combinations of fairness and utilitarian objectives are modelled as conic programs with an exponential number of variables. We propose a column generation approach to solve them effectively in practice. Finally, we make an extensive comparison of the different schemes in terms of the balance of utility and fairness score, and validate the scalability of our methodology for benchmark instances from the literature.
2024-12-31
European Journal of Operational Research (publié)
doi.org
arxiv.org
A Learning-Based Framework for Fair and Scalable Solution Generation in Kidney Exchange Problems
William St-Arnaud
Margarida Carvalho
Golnoosh Farnadi
2024-12-31
Trans. Mach. Learn. Res. (publié)
openreview.net
openreview.net
Learning to Build Solutions in Stochastic Matching Problems Using Flows (Student Abstract)
William St-Arnaud
Margarida Carvalho
Golnoosh Farnadi
Generative Flow Networks, known as GFlowNets, have been introduced in recent times, presenting an exciting possibility for neural networks t… (voir plus)o model distributions across various data structures. In this paper, we broaden their applicability to encompass scenarios where the data structures are optimal solutions of a combinatorial problem. Concretely, we propose the use of GFlowNets to learn the distribution of optimal solutions for kidney exchange problems (KEPs), a generalized form of matching problems involving cycles.
2024-03-23
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
doi.org
Individual Fairness in Kidney Exchange Programs
Golnoosh Farnadi
William St-Arnaud
Behrouz Babaki
Margarida Carvalho
Kidney transplant is the preferred method of treatment for patients suffering from kidney failure. However, not all patients can find a dono… (voir plus)r which matches their physiological characteristics. Kidney exchange programs (KEPs) seek to match such incompatible patient-donor pairs together, usually with the main objective of maximizing the total number of transplants. Since selecting one optimal solution translates to a decision on who receives a transplant, it has a major effect on the lives of patients. The current practice in selecting an optimal solution does not necessarily ensure fairness in the selection process. In this paper, the existence of multiple optimal plans for a KEP is explored as a mean to achieve individual fairness. We propose the use of randomized policies for selecting an optimal solution in which patients' equal 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. The advantages of our proposed method over the common practice of using the optimal solution obtained by a solver are stressed through computational experiments. Our methodology enables decision makers to fully control KEP outcomes, overcoming any potential bias or vulnerability intrinsic to a deterministic solver.
2021-05-17
AAAI Conference on Artificial Intelligence (publié)
doi.org