Flow-based generative models for Markov chain Monte Carlo in lattice field theory. - Michael S. Albergo; Gurtej Kanwar; Phiala E. Shanahan (CoRR, 2019)

Normalizing Flows on Tori and Spheres. - Danilo Jimenez Rezende; George Papamakarios; Sébastien Racanière; Michael S. Albergo; Gurtej Kanwar; Phiala E. Shanahan; Kyle Cranmer (ICML, 2020)

Equivariant flow-based sampling for lattice gauge theory. - Gurtej Kanwar; Michael S. Albergo; Denis Boyda; Kyle Cranmer; Daniel C. Hackett; Sébastien Racanière; Danilo Jimenez Rezende; Phiala E. Shanahan (CoRR, 2020)

Sampling using SU(N) gauge equivariant flows. - Denis Boyda; Gurtej Kanwar; Sébastien Racanière; Danilo Jimenez Rezende; Michael S. Albergo; Kyle Cranmer; Daniel C. Hackett; Phiala E. Shanahan (CoRR, 2020)

Introduction to Normalizing Flows for Lattice Field Theory. - Michael S. Albergo; Denis Boyda; Daniel C. Hackett; Gurtej Kanwar; Kyle Cranmer; Sébastien Racanière; Danilo Jimenez Rezende; Phiala E. Shanahan (CoRR, 2021)

Flow-based sampling for fermionic lattice field theories. - Michael S. Albergo; Gurtej Kanwar; Sébastien Racanière; Danilo J. Rezende; Julian M. Urban; Denis Boyda; Kyle Cranmer; Daniel C. Hackett; Phiala E. Shanahan (CoRR, 2021)

Flow-based sampling for multimodal distributions in lattice field theory. - Daniel C. Hackett; Chung-Chun Hsieh; Michael S. Albergo; Denis Boyda; Jiunn-Wei Chen; Kai-Feng Chen; Kyle Cranmer; Gurtej Kanwar; Phiala E. Shanahan (CoRR, 2021)

Flow-based sampling in the lattice Schwinger model at criticality. - Michael S. Albergo; Denis Boyda; Kyle Cranmer; Daniel C. Hackett; Gurtej Kanwar; Sébastien Racanière; Danilo J. Rezende; Fernando Romero-López; Phiala E. Shanahan; Julian M. Urban (CoRR, 2022)

Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions. - Ryan Abbott; Michael S. Albergo; Denis Boyda; Kyle Cranmer; Daniel C. Hackett; Gurtej Kanwar; Sébastien Racanière; Danilo J. Rezende; Fernando Romero-López; Phiala E. Shanahan; Betsy Tian; Julian M. Urban (CoRR, 2022)

Aspects of scaling and scalability for flow-based sampling of lattice QCD. - Ryan Abbott; Michael S. Albergo; Aleksandar Botev; Denis Boyda; Kyle Cranmer; Daniel C. Hackett; Alexander G. de G. Matthews; Sébastien Racanière; Ali Razavi; Danilo J. Rezende; Fernando Romero-López; Phiala E. Shanahan; et al. (CoRR, 2022)

Building Normalizing Flows with Stochastic Interpolants. - Michael S. Albergo; Eric Vanden-Eijnden (ICLR, 2023)

Stochastic Interpolants: A Unifying Framework for Flows and Diffusions. - Michael S. Albergo; Nicholas M. Boffi; Eric Vanden-Eijnden (CoRR, 2023)

Normalizing flows for lattice gauge theory in arbitrary space-time dimension. - Ryan Abbott; Michael S. Albergo; Aleksandar Botev; Denis Boyda; Kyle Cranmer; Daniel C. Hackett; Gurtej Kanwar; Alexander G. de G. Matthews; Sébastien Racanière; Ali Razavi; Danilo J. Rezende; Fernando Romero-López; et al. (CoRR, 2023)

Learning to Sample Better. - Michael S. Albergo; Eric Vanden-Eijnden (CoRR, 2023)

SiT: Exploring Flow and Diffusion-Based Generative Models with Scalable Interpolant Transformers. - Nanye Ma; Mark Goldstein; Michael S. Albergo; Nicholas M. Boffi; Eric Vanden-Eijnden; Saining Xie (ECCV (77), 2024)

Multimarginal Generative Modeling with Stochastic Interpolants. - Michael S. Albergo; Nicholas Matthew Boffi; Michael Lindsey; Eric Vanden-Eijnden (ICLR, 2024)

Stochastic Interpolants with Data-Dependent Couplings. - Michael S. Albergo; Mark Goldstein; Nicholas Matthew Boffi; Rajesh Ranganath; Eric Vanden-Eijnden (ICML, 2024)

Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes. - Yifan Chen; Mark Goldstein; Mengjian Hua; Michael S. Albergo; Nicholas Matthew Boffi; Eric Vanden-Eijnden (ICML, 2024)

Practical applications of machine-learned flows on gauge fields. - Ryan Abbott; Michael S. Albergo; Denis Boyda; Daniel C. Hackett; Gurtej Kanwar; Fernando Romero-López; Phiala E. Shanahan; Julian M. Urban (CoRR, 2024)

Flow Map Matching. - Nicholas M. Boffi; Michael S. Albergo; Eric Vanden-Eijnden (CoRR, 2024)

NETS: A Non-Equilibrium Transport Sampler. - Michael S. Albergo; Eric Vanden-Eijnden (CoRR, 2024)

Flow map matching with stochastic interpolants: A mathematical framework for consistency models. - Nicholas Matthew Boffi; Michael Samuel Albergo; Eric Vanden-Eijnden (Trans. Mach. Learn. Res., 2025)

Debiasing Guidance for Discrete Diffusion with Sequential Monte Carlo. - Cheuk Kit Lee; Paul Jeha; Jes Frellsen; Pietro Lio; Michael Samuel Albergo; Francisco Vargas 001 (CoRR, 2025)

LEAPS: A discrete neural sampler via locally equivariant networks. - Peter Holderrieth; Michael S. Albergo; Tommi S. Jaakkola (CoRR, 2025)

How to build a consistency model: Learning flow maps via self-distillation. - Nicholas M. Boffi; Michael S. Albergo; Eric Vanden-Eijnden (CoRR, 2025)

Multitask Learning with Stochastic Interpolants. - Hugo Negrel; Florentin Coeurdoux; Michael S. Albergo; Eric Vanden-Eijnden (CoRR, 2025)