Pablo Lemos

Posterior Sampling of the Initial Conditions of the Universe from Non-linear Large Scale Structures using Score-Based Generative Models

Ronan Legin

Matthew Ho

Pablo Lemos

Laurence Perreault-Levasseur

Shirley Ho

Yashar Hezaveh

Benjamin Wandelt

2023-10-13

Monthly Notices of the Royal Astronomical Society: Letters (published)

doi.org

arxiv.org

Sampling-Based Accuracy Testing of Posterior Estimators for General Inference

Pablo Lemos

Adam Coogan

Yashar Hezaveh

Laurence Perreault-Levasseur

2023-07-03

Proceedings of the 40th International Conference on Machine Learning (published)

doi.org

openreview.net

Sampling-Based Accuracy Testing of Posterior Estimators for General Inference

Pablo Lemos

Adam Coogan

Yashar Hezaveh

Laurence Perreault-Levasseur

2023-04-24

ICML.cc/2023/Conference (poster)

doi.org

openreview.net

A theory of continuous generative flow networks

Alex Hernandez-Garcia

Lena Nehale Ezzine

Yoshua Bengio

Nikolay Malkin

Generative flow networks (GFlowNets) are amortized variational inference algorithms that are trained to sample from unnormalized target dist… (see more)ributions over compositional objects. A key limitation of GFlowNets until this time has been that they are restricted to discrete spaces. We present a theory for generalized GFlowNets, which encompasses both existing discrete GFlowNets and ones with continuous or hybrid state spaces, and perform experiments with two goals in mind. First, we illustrate critical points of the theory and the importance of various assumptions. Second, we empirically demonstrate how observations about discrete GFlowNets transfer to the continuous case and show strong results compared to non-GFlowNet baselines on several previously studied tasks. This work greatly widens the perspectives for the application of GFlowNets in probabilistic inference and various modeling settings.

2023-01-30

ArXiv (preprint)

doi.org

arxiv.org

A theory of continuous generative flow networks

Alex Hernandez-Garcia

Lena Nehale Ezzine

Yoshua Bengio

Nikolay Malkin

Generative flow networks (GFlowNets) are amortized variational inference algorithms that are trained to sample from unnormalized target dist… (see more)ributions over compositional objects. A key limitation of GFlowNets until this time has been that they are restricted to discrete spaces. We present a theory for generalized GFlowNets, which encompasses both existing discrete GFlowNets and ones with continuous or hybrid state spaces, and perform experiments with two goals in mind. First, we illustrate critical points of the theory and the importance of various assumptions. Second, we empirically demonstrate how observations about discrete GFlowNets transfer to the continuous case and show strong results compared to non-GFlowNet baselines on several previously studied tasks. This work greatly widens the perspectives for the application of GFlowNets in probabilistic inference and various modeling settings.

2023-01-01

ICML (published)

doi.org

openreview.net

A theory of continuous generative flow networks

Alex Hernandez-Garcia

Lena Nehale Ezzine