Perreault-Levasseur, Laurence

10 publications

ICLR 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, Yashar Hezaveh

ICLRW 2025 Solving Bayesian Inverse Problems with Diffusion Priors and Off-Policy RL Luca Scimeca, Siddarth Venkatraman, Moksh Jain, Minsu Kim, Marcin Sendera, Mohsin Hasan, Alexandre Adam, Yashar Hezaveh, Laurence Perreault-Levasseur, Yoshua Bengio, Glen Berseth, Nikolay Malkin

ICMLW 2024 Assessing the Viability of Generative Modeling in Simulated Astronomical Observations Patrick Janulewicz, Laurence Perreault-Levasseur, Tracy Webb

ICML 2024 Improving Gradient-Guided Nested Sampling for Posterior Inference Pablo Lemos, Nikolay Malkin, Will Handley, Yoshua Bengio, Yashar Hezaveh, Laurence Perreault-Levasseur

ICMLW 2024 Inpainting Galaxy Counts onto N-Body Simulations over Multiple Cosmologies and Astrophysics Antoine Bourdin, Ronan Legin, Matthew Ho, Alexandre Adam, Yashar Hezaveh, Laurence Perreault-Levasseur

ICMLW 2024 Neural Ratio Estimators Meet Distributional Shift and Mode Misspecification: A Cautionary Tale from Strong Gravitational Lensing Andreas Filipp, Yashar Hezaveh, Laurence Perreault-Levasseur

ICMLW 2023 Lie Point Symmetry and Physics Informed Networks Tara Akhound-Sadegh, Laurence Perreault-Levasseur, Johannes Brandstetter, Max Welling, Siamak Ravanbakhsh

NeurIPS 2023 Lie Point Symmetry and Physics-Informed Networks Tara Akhound-Sadegh, Laurence Perreault-Levasseur, Johannes Brandstetter, Max Welling, Siamak Ravanbakhsh

ICML 2023 Sampling-Based Accuracy Testing of Posterior Estimators for General Inference Pablo Lemos, Adam Coogan, Yashar Hezaveh, Laurence Perreault-Levasseur

NeurIPSW 2023 Score-Based Likelihood Characterization for Inverse Problems in the Presence of Non-Gaussian Noise Ronan Legin, Alexandre Adam, Yashar Hezaveh, Laurence Perreault-Levasseur