HIGAN: Cosmic Neutral Hydrogen with Generative Adversarial Networks. - Juan Zamudio-Fernandez; Atakan Okan; Francisco Villaescusa-Navarro; Seda Bilaloglu; Asena Derin Cengiz; Siyu He; Laurence Perreault Levasseur; Shirley Ho (CoRR, 2019)

Bayesian Neural Networks. - Tom Charnock; Laurence Perreault Levasseur; François Lanusse (CoRR, 2020)

deep21: a Deep Learning Method for 21cm Foreground Removal. - T. Lucas Makinen; Lachlan Lancaster; Francisco Villaescusa-Navarro; Peter Melchior; Shirley Ho; Laurence Perreault Levasseur; David N. Spergel (CoRR, 2020)

Posterior samples of source galaxies in strong gravitational lenses with score-based priors. - Alexandre Adam; Adam Coogan; Nikolay Malkin; Ronan Legin; Laurence Perreault Levasseur; Yashar Hezaveh; Yoshua Bengio (CoRR, 2022)

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

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

Pixelated Reconstruction of Foreground Density and Background Surface Brightness in Gravitational Lensing Systems using Recurrent Inference Machines. - Alexandre Adam; Laurence Perreault Levasseur; Yashar Hezaveh; Max Welling (CoRR, 2023)

Echoes in the Noise: Posterior Samples of Faint Galaxy Surface Brightness Profiles with Score-Based Likelihoods and Priors. - Alexandre Adam; Connor Stone; Connor Bottrell; Ronan Legin; Yashar Hezaveh; Laurence Perreault Levasseur (CoRR, 2023)

Bayesian Imaging for Radio Interferometry with Score-Based Priors. - Noe Dia; M. J. Yantovski-Barth; Alexandre Adam; Micah Bowles; Pablo Lemos; Anna M. M. Scaife; Yashar Hezaveh; Laurence Perreault Levasseur (CoRR, 2023)

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

PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimation. - Pablo Lemos; Sammy Sharief; Nikolay Malkin; Laurence Perreault Levasseur; Yashar Hezaveh (CoRR, 2024)

Tackling the Problem of Distributional Shifts: Correcting Misspecified, High-Dimensional Data-Driven Priors for Inverse Problems. - Gabriel Missael Barco; Alexandre Adam; Connor Stone; Yashar Hezaveh; Laurence Perreault Levasseur (CoRR, 2024)

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 (J. Open Source Softw., 2024)

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 (ICLR, 2025)

IRIS: A Bayesian Approach for Image Reconstruction in Radio Interferometry with expressive Score-Based priors. - Noe Dia; M. J. Yantovski-Barth; Alexandre Adam; Micah Bowles; Laurence Perreault Levasseur; Yashar Hezaveh; Anna M. M. Scaife (CoRR, 2025)

Solving Bayesian inverse problems with diffusion priors and off-policy RL. - Luca Scimeca; Siddarth Venkatraman; Moksh Jain; Minsu Kim 004; Marcin Sendera; Mohsin Hasan; Luke Rowe; Sarthak Mittal; Pablo Lemos; Emmanuel Bengio; Alexandre Adam; Jarrid Rector-Brooks; et al. (CoRR, 2025)