no code implementations • 5 Feb 2024 • Yu-Guan Hsieh, James Thornton, Eugene Ndiaye, Michal Klein, Marco Cuturi, Pierre Ablin
Beyond minimizing a single training loss, many deep learning estimation pipelines rely on an auxiliary objective to quantify and encourage desirable properties of the model (e. g. performance on another dataset, robustness, agreement with a prior).
no code implementations • 7 Jul 2022 • James Thornton, Michael Hutchinson, Emile Mathieu, Valentin De Bortoli, Yee Whye Teh, Arnaud Doucet
Our proposed method generalizes Diffusion Schr\"odinger Bridge introduced in \cite{debortoli2021neurips} to the non-Euclidean setting and extends Riemannian score-based models beyond the first time reversal.
no code implementations • 15 Jun 2022 • James Thornton, Marco Cuturi
While the optimal transport (OT) problem was originally formulated as a linear program, the addition of entropic regularization has proven beneficial both computationally and statistically, for many applications.
2 code implementations • 6 Feb 2022 • Valentin De Bortoli, Emile Mathieu, Michael Hutchinson, James Thornton, Yee Whye Teh, Arnaud Doucet
Score-based generative models (SGMs) are a powerful class of generative models that exhibit remarkable empirical performance.
1 code implementation • 14 Nov 2021 • Jeremy Heng, Valentin De Bortoli, Arnaud Doucet, James Thornton
This is known to be a challenging problem that has received much attention in the last two decades.
2 code implementations • NeurIPS 2021 • Valentin De Bortoli, James Thornton, Jeremy Heng, Arnaud Doucet
In contrast, solving the Schr\"odinger Bridge problem (SB), i. e. an entropy-regularized optimal transport problem on path spaces, yields diffusions which generate samples from the data distribution in finite time.
1 code implementation • 15 Feb 2021 • Adrien Corenflos, James Thornton, George Deligiannidis, Arnaud Doucet
Particle Filtering (PF) methods are an established class of procedures for performing inference in non-linear state-space models.