Gravitational-wave parameter estimation with autoregressive neural network flows. - Stephen R. Green; Christine Simpson; Jonathan Gair (CoRR, 2020)

Real-time gravitational-wave science with neural posterior estimation. - Maximilian Dax; Stephen R. Green; Jonathan Gair; Jakob H. Macke; Alessandra Buonanno; Bernhard Schölkopf (CoRR, 2021)

Complete parameter inference for GW150914 using deep learning. - Stephen R. Green; Jonathan Gair (Mach. Learn. Sci. Technol., 2021)

Group equivariant neural posterior estimation. - Maximilian Dax; Stephen R. Green; Jonathan Gair; Michael Deistler; Bernhard Schölkopf; Jakob H. Macke (ICLR, 2022)

Using machine learning to parametrize postmerger signals from binary neutron stars. - Tim Whittaker; William E. East; Stephen R. Green; Luis Lehner; Huan Yang (CoRR, 2022)

Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference. - Maximilian Dax; Stephen R. Green; Jonathan Gair; Michael Pürrer; Jonas Wildberger; Jakob H. Macke; Alessandra Buonanno; Bernhard Schölkopf (CoRR, 2022)

Adapting to noise distribution shifts in flow-based gravitational-wave inference. - Jonas Wildberger; Maximilian Dax; Stephen R. Green; Jonathan Gair; Michael Pürrer; Jakob H. Macke; Alessandra Buonanno; Bernhard Schölkopf (CoRR, 2022)

Flow Matching for Scalable Simulation-Based Inference. - Jonas Wildberger; Maximilian Dax; Simon Buchholz; Stephen R. Green; Jakob H. Macke; Bernhard Schölkopf (NeurIPS, 2023)

Real-time gravitational-wave inference for binary neutron stars using machine learning. - Maximilian Dax; Stephen R. Green; Jonathan Gair; Nihar Gupte; Michael Pürrer; Vivien Raymond; Jonas Wildberger; Jakob H. Macke; Alessandra Buonanno; Bernhard Schölkopf (CoRR, 2024)

Real-time inference for binary neutron star mergers using machine learning. - Maximilian Dax; Stephen R. Green; Jonathan Gair; Nihar Gupte; Michael Pürrer; Vivien Raymond; Jonas Wildberger; Jakob H. Macke; Alessandra Buonanno; Bernhard Schölkopf (Nat., 2025)