Pablo Lemos

According to our database, Pablo Lemos authored at least 14 papers between 2022 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

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

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, Viraj Pandya, Shivam Pandey, Lucia A. Perez, Benjamin D. Wandelt, Greg L. Bryan

CoRR, 2024

On diffusion models for amortized inference: Benchmarking and improving stochastic control and sampling.

Marcin Sendera, Minsu Kim, Sarthak Mittal, Pablo Lemos, Luca Scimeca, Jarrid Rector-Brooks, Alexandre Adam, Yoshua Bengio, Nikolay Malkin

CoRR, 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

2023

Rediscovering orbital mechanics with machine learning.

Pablo Lemos, Niall Jeffrey, Miles D. Cranmer, Shirley Ho, Peter W. Battaglia

Mach. Learn. Sci. Technol., December, 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., March, 2023

Improving Gradient-guided Nested Sampling for Posterior Inference.

Pablo Lemos, Nikolay Malkin, Will Handley, Yoshua Bengio, 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

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, Azadeh Moradinezhad Dizgah, Bruno Régaldo-Saint Blancard, David N. Spergel

CoRR, 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

Sampling-Based Accuracy Testing of Posterior Estimators for General Inference.

Pablo Lemos, Adam Coogan, Yashar Hezaveh, Laurence Perreault Levasseur

Proceedings of the International Conference on Machine Learning, 2023

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

Proceedings of the International Conference on Machine Learning, 2023

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

Split personalities in Bayesian Neural Networks: the case for full marginalisation.

David Yallup, Will Handley, Mike Hobson, Anthony N. Lasenby, Pablo Lemos

CoRR, 2022