Split personalities in Bayesian Neural Networks: the case for full marginalisation. - David Yallup; Will Handley; Mike Hobson; Anthony N. Lasenby; Pablo Lemos (CoRR, 2022)

Removing the fat from your posterior samples with margarine. - Harry T. J. Bevins; William J. Handley; Pablo Lemos; Peter H. Sims; Eloy de Lera Acedo; Anastasia Fialkov; Justin Alsing (CoRR, 2022)

A theory of continuous generative flow networks. - Salem Lahlou; Tristan Deleu; Pablo Lemos; Dinghuai Zhang; Alexandra Volokhova; Alex Hernández-García; Léna Néhale Ezzine; Yoshua Bengio; Nikolay Malkin (ICML, 2023)

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

Towards equilibrium molecular conformation generation with GFlowNets. - Alexandra Volokhova; Michal Koziarski; Alex Hernández-García; Cheng-Hao Liu; Santiago Miret; Pablo Lemos; Luca A. Thiede; Zichao Yan; Alán Aspuru-Guzik; Yoshua Bengio (CoRR, 2023)

SimBIG: Field-level Simulation-Based Inference of Galaxy Clustering. - Pablo Lemos; Liam Holden Parker; ChangHoon Hahn; Shirley Ho; Michael Eickenberg; Jiamin Hou; Elena Massara; Chirag Modi 002; Azadeh Moradinezhad Dizgah; Bruno Régaldo-Saint Blancard; David N. Spergel (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)

Robust simulation-based inference in cosmology with Bayesian neural networks. - Pablo Lemos; Miles D. Cranmer; Muntazir Abidi; ChangHoon Hahn; Michael Eickenberg; Elena Massara; David Yallup; Shirley Ho (Mach. Learn. Sci. Technol., 2023)

Rediscovering orbital mechanics with machine learning. - Pablo Lemos; Niall Jeffrey; Miles D. Cranmer; Shirley Ho; Peter W. Battaglia (Mach. Learn. Sci. Technol., 2023)

Iterated Denoising Energy Matching for Sampling from Boltzmann Densities. - Tara Akhound-Sadegh; Jarrid Rector-Brooks; Avishek Joey Bose; Sarthak Mittal; Pablo Lemos; Cheng-Hao Liu; Marcin Sendera; Siamak Ravanbakhsh; Gauthier Gidel; Yoshua Bengio; Nikolay Malkin; Alexander Tong 001 (ICML, 2024)

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

Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Generation. - Guillaume Huguet; James Vuckovic; Kilian Fatras; Eric Thibodeau-Laufer; Pablo Lemos; Riashat Islam; Cheng-Hao Liu; Jarrid Rector-Brooks; Tara Akhound-Sadegh; Michael M. Bronstein; Alexander Tong 001; Avishek Joey Bose (NeurIPS, 2024)

Improved off-policy training of diffusion samplers. - Marcin Sendera; Minsu Kim 004; Sarthak Mittal; Pablo Lemos; Luca Scimeca; Jarrid Rector-Brooks; Alexandre Adam; Yoshua Bengio; Nikolay Malkin (NeurIPS, 2024)

Amortizing intractable inference in diffusion models for vision, language, and control. - Siddarth Venkatraman; Moksh Jain; Luca Scimeca; Minsu Kim 004; Marcin Sendera; Mohsin Hasan; Luke Rowe; Sarthak Mittal; Pablo Lemos; Emmanuel Bengio; Alexandre Adam; Jarrid Rector-Brooks; et al. (NeurIPS, 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)

On diffusion models for amortized inference: Benchmarking and improving stochastic control and sampling. - Marcin Sendera; Minsu Kim 004; Sarthak Mittal; Pablo Lemos; Luca Scimeca; Jarrid Rector-Brooks; Alexandre Adam; Yoshua Bengio; Nikolay Malkin (CoRR, 2024)

LtU-ILI: An All-in-One Framework for Implicit Inference in Astrophysics and Cosmology. - Matthew Ho; Deaglan J. Bartlett; Nicolas Chartier; Carolina Cuesta-Lázaro; Simon Ding; Axel Lapel; Pablo Lemos; Christopher C. Lovell; T. Lucas Makinen; Chirag Modi 002; Viraj Pandya; Shivam Pandey; et al. (CoRR, 2024)

Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone Generation. - Guillaume Huguet; James Vuckovic; Kilian Fatras; Eric Thibodeau-Laufer; Pablo Lemos; Riashat Islam; Cheng-Hao Liu; Jarrid Rector-Brooks; Tara Akhound-Sadegh; Michael M. Bronstein; Alexander Tong 001; Avishek Joey Bose (CoRR, 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)

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)