Publications

Lloyd's $K$-Means Clustering Algorithm is Frank-Wolfe in Disguise
Michael Pokojovy
J. Marcus Jobe
Simon Lacoste-Julien
Lloyd's …
2026-02-02
International Conference on Artificial Intelligence and Statistics (poster)
openreview.net
openreview.net
Local Inconsistency Resolution: The Interplay between Attention and Control in Probabilistic Models
Oliver Ethan Richardson
Abdessamad EL KABID
Mandana Samiei
Mehran Shakerinava
Joseph D Viviano
Ali Parviz
Yoshua Bengio
We present a generic algorithm for learning and approximate inference with an intuitive epistemic interpretation: iteratively focus on a sub… (see more)set of the model and resolve inconsistencies using the parameters under control. This framework, which we call Local Inconsistency Resolution (LIR) is built upon Probabilistic Dependency Graphs (PDGs), which provide a flexible representational foundation capable of capturing inconsistent beliefs. We show how LIR unifies and generalizes a wide variety of important algorithms in the literature, including the Expectation-Maximization (EM) algorithm, belief propagation, adversarial training, GANs, and GFlowNets. Each of these methods can be recovered as a specific instance of LIR by choosing a procedure to direct focus (attention and control). We implement this algorithm for discrete PDGs and study its properties on synthetically generated PDGs, comparing its behavior to the global optimization semantics of the full PDG.
2026-02-02
International Conference on Artificial Intelligence and Statistics (spotlight)
openreview.net
openreview.net
A multi-ancestry genetic reference for the Quebec population
Peyton McClelland
Georgette Femerling
Rose Laflamme
Alejandro Mejia-Garcia
Mohadese Sayahian Dehkordi
Hongyu Xiao
Alex Diaz-Papkovich
Justin Pelletier
Jean Christophe Grenier
Ken Sin Lo
Luke Anderson-Trocmé
Justin Bellavance
Vincent Chapdelaine
Genevieve Gagnon
Annelie De Mori
Gerardo Martinez
Kristen Mohler
Thibault de Malliard
Catherine Labbé
Marjorie Labrecque … (see 14 more)
Alexandre Montpetit
Dan Spiegelman
Guy A Rouleau
Jean-François Théroux
Hufeng Zhou
Simon L Girard
Julie G Hussin
Anne-Marie Laberge
Claude Bhérer
Martine Tétreault
Sarah A Gagliano Taliun
Daniel Taliun
Simon Gravel
Guillaume Lettre
While international efforts have characterized genetic variation in millions of individuals, the interplay of environmental, social, cultura… (see more)l, and genetic factors is poorly understood for most worldwide populations. The province of Quebec in Canada has been the site of numerous genetic studies, often focusing on individual Mendelian diseases in founder sub-populations. Here, we profiled and analyzed genome-wide genotyped variation in 29,337 Quebec residents from the large population-based cohort CARTaGENE (CaG), including rich phenotype and environmental data. We also sequenced the whole-genome of 2,173 CaG participants, including 163 and 132 individuals with grandparents born in Haiti and Morocco, respectively. We use this genetic information to gain insight into Quebec's demography and to help interpret the potential significance of variants identified in clinically important genes. We built an imputation panel by phasing the CaG whole-genome sequence data and showed, using genome-wide association studies (GWAS), how it improves the discovery of phenotype-genotype associations in this population. We provide allele frequency information and GWAS results through dedicated and publicly available websites. The genetic data, paired with phenotypic and environmental information, is also available for research use upon scientific and ethical review.
2026-02-02
Nature Communications (published)
doi.org
Observational Study of Maternal and Fetal Outcome in Posterior Reversible Encephalopathy Syndrome in Eclamptic Women in a Tertiary Care Institute
Prerna Kailashchand Gupta
Meenal Shailesh Sarmalkar
Madhuri A Mehendale
Aishwarya Agrawal
2026-02-02
Journal of South Asian Federation of Obstetrics and Gynaecology (published)
doi.org
Robust Intervention Learning from Emergency Stop Interventions
Ethan Pronovost
Khimya Khetarpal
Siddhartha Srinivasa
2026-02-02
ArXiv (preprint)
arxiv.org
arxiv.org
The Role of Causal Features in Strategic Classification for Robustness and Alignment
Sophia Gunluk
Antonio Gois
Nir Rosenfeld
Nidhi Hegde
Simon Lacoste-Julien
Dhanya Sridhar
In strategic classification, an institution (e.g., a bank) anticipates adaptation from users who change their features to increase utility i… (see more)n a classification task (e.g., loan repayment). Since a key challenge is the distribution shift induced by users, we turn to causal models, which have been shown to bound the worst-case out-of-distribution (OOD) risk, and establish several new results that link causality and strategic classification. First, we show that causal classification leads to optimal classification error after any sufficiently large adaptation, when the noise is bounded in a certain way. Second, when these assumptions do not hold, we show OOD cross-entropy risk of optimal classifiers decomposes into an OOD bias term and a term arising from not using all observable features, allowing us to determine when causal classifiers have an advantage. Finally, we show that causal classifiers can align long-term incentives between institutions and users, contrasting with previous work that highlights social costs of such approaches. We validate our theory empirically on synthetic data, finding that our results predict behavior in practice.
2026-02-02
Artificial Intelligence and Statistics (poster)
openreview.net
openreview.net
On the Role of Depth in the Expressivity of RNNs
Maude Lizaire
Michael Rizvi-Martel
Éric Dupuis
Guillaume Rabusseau
The benefits of depth in feedforward neural networks (FNNs) are well known: composing multiple layers of linear transformations with nonline… (see more)ar activations enables complex computations. While similar effects are expected in recurrent neural networks (RNNs), it remains unclear how depth interacts with recurrence to shape expressive power. Here, we formally show that depth increases RNNs’ memory capacity efficiently with respect to parameters, enhancing expressivity both by enabling more complex input transformations and improving the retention of past information. We extend our analysis to 2RNNs, a generalization of RNNs with multiplicative interactions between inputs and hidden states. Unlike RNNs, which remain linear without nonlinear activations, 2RNNs perform polynomial transformations whose maximal degree grows with depth. We further show that multiplicative interactions cannot, in general, be replaced by layerwise nonlinearities. Finally, we validate these insights empirically on synthetic and real-world tasks.
2026-02-02
International Conference on Artificial Intelligence and Statistics (spotlight)
doi.org
openreview.net
Tractable Shapley Values and Interactions via Tensor Networks
Farzaneh Heidari
Chao Li
Guillaume Rabusseau
We show how to replace the …
2026-02-02
Artificial Intelligence and Statistics (poster)
doi.org
openreview.net
How Notations Evolve: A Historical Analysis with Implications for Supporting User-Defined Abstractions
jingyue zhang
J.D. Zamfirescu-Pereira
Elena L. Glassman
Damien Masson
Ian Arawjo
2026-02-01
ArXiv (preprint)
arxiv.org
arxiv.org
Opposite impact of thermal expansion and phonon anharmonicity on the phonon-limited resistivity of elemental metals from first principles
Ao Wang
Junwen Yin
Félix Antoine Goudreault
Michel Côté
Olle Hellman
Samuel Poncé
Understanding electrical resistivity in metals remains a central challenge in quantifying charge transport at finite temperature. Current fi… (see more)rst-principles calculations based on the Boltzmann transport equation often match experiments, yet they almost always neglect the effect of thermal expansion and phonon anharmonicity. We show that both effects exert an opposite impact on electron-phonon coupling and on electrical resistivity. Thermal expansion enhances the coupling and leads to overestimation of resistivity, whereas anharmonic effects reduce it. By explicitly incorporating both effects, we establish a more complete description of resistivity in elemental metals, demonstrated here for Pb, Nb, and Al.
2026-02-01
Physical Review B (published)
doi.org
arxiv.org
Revisiting Generalization Measures Beyond IID: An Empirical Study under Distributional Shift
Sora Nakai
Youssef Fadhloun
Kacem Mathlouthi
Kotaro Yoshida
Ganesh Talluri
Ioannis Mitliagkas
Hiroki Naganuma
Generalization remains a central yet unresolved challenge in deep learning, particularly the ability to predict a model's performance beyond… (see more) its training distribution using quantities available prior to test-time evaluation. Building on the large-scale study of Jiang et al. (2020). and concerns by Dziugaite et al. (2020). about instability across training configurations, we benchmark the robustness of generalization measures beyond IID regime. We train small-to-medium models over 10,000 hyperparameter configurations and evaluate more than 40 measures computable from the trained model and the available training data alone. We significantly broaden the experimental scope along multiple axes: (i) extending the evaluation beyond the standard IID setting to include benchmarking for robustness across diverse distribution shifts, (ii) evaluating multiple architectures and training recipes, and (iii) newly incorporating calibration- and information-criteria-based measures to assess their alignment with both IID and OOD generalization. We find that distribution shifts can substantially alter the predictive performance of many generalization measures, while a smaller subset remains comparatively stable across settings.
2026-02-01
Open MIND (preprint)
doi.org
arxiv.org
Sub-optimality bounds for certainty equivalent policies in partially observed systems
Berk Bozkurt
Aditya Mahajan
Ashutosh Nayyar
Yi Ouyang
In this paper, we present a generalization of the certainty equivalence principle of stochastic control. One interpretation of the classical… (see more) certainty equivalence principle for linear systems with output feedback and quadratic costs is as follows: the optimal action at each time is obtained by evaluating the optimal state-feedback policy of the stochastic linear system at the minimum mean square error (MMSE) estimate of the state. Motivated by this interpretation, we consider certainty equivalent policies for general (non-linear) partially observed stochastic systems that allow for any state estimate rather than restricting to MMSE estimates. In such settings, the certainty equivalent policy is not optimal. For models where the cost and the dynamics are smooth in an appropriate sense, we derive upper bounds on the sub-optimality of certainty equivalent policies. We present several examples to illustrate the results.
2026-02-01
ArXiv (preprint)
arxiv.org
arxiv.org