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Laurence Perreault-Levasseur

5 accepted papers

2026

MIRA: A Score for Conditional Distribution Accuracy and Model Comparison

Sammy Sharief, Justine Zeghal, Gabriel Missael Barco, Pablo Lemos, Yashar Hezaveh, Laurence Perreault-Levasseur
ICML 2026spotlight

We present Mira, a method for estimating the expected probability that samples from a candidate conditional distribution match the true, unknown conditional distribution, for which only data-label pairs are available. We derive theoretical bounds obtained when the candidate distribution matches the …

Cited by 0Source ↗Scholar ↗
2025

PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimation

Pablo Lemos, Sammy Nasser Sharief, Nikolay Malkin, Salma Salhi, Connor Stone, Laurence Perreault-Levasseur +1
ICLR 2025poster

We propose a likelihood-free method for comparing two distributions given samples from each, with the goal of assessing the quality of generative models. The proposed approach, PQMass, provides a statistically rigorous method for assessing the performance of a single generative model or the comparis…

Cited by 3Source ↗PDF ↗Scholar ↗
2024

Improving Gradient-Guided Nested Sampling for Posterior Inference

Pablo Lemos, Nikolay Malkin, Will Handley, Yoshua Bengio, Yashar Hezaveh, Laurence Perreault-Levasseur
ICML 2024poster

We present a performant, general-purpose gradient-guided nested sampling (GGNS) algorithm, combining the state of the art in differentiable programming, Hamiltonian slice sampling, clustering, mode separation, dynamic nested sampling, and parallelization. This unique combination allows GGNS to scale…

Cited by 6Source ↗PDF ↗code ↗Scholar ↗
2023

Lie Point Symmetry and Physics-Informed Networks

Tara Akhound-Sadegh, Laurence Perreault-Levasseur, Johannes Brandstetter, Max Welling, Siamak Ravanbakhsh
NeurIPS 2023poster

Symmetries have been leveraged to improve the generalization of neural networks through different mechanisms from data augmentation to equivariant architectures. However, despite their potential, their integration into neural solvers for partial differential equations (PDEs) remains largely unexplor…

Cited by 17Source ↗PDF ↗Scholar ↗
2023

Sampling-Based Accuracy Testing of Posterior Estimators for General Inference

Pablo Lemos, Adam Coogan, Yashar Hezaveh, Laurence Perreault-Levasseur
ICML 2023poster

Parameter inference, i.e. inferring the posterior distribution of the parameters of a statistical model given some data, is a central problem to many scientific disciplines. Posterior inference with generative models is an alternative to methods such as Markov Chain Monte Carlo, both for likelihood-…

Cited by 50Source ↗PDF ↗code ↗Scholar ↗