2 citations · 3 across the 6 of their papers we have counts for
6 papers
The hierarchical barycenter: conditional probability simulation with structured and unobserved covariates
Esteban G. Tabak, Giulio Trigila, Wenjun Zhao
This paper presents a new method for conditional probability density simulation. The method is design to work with unstructured data set when data are not characterized by the same…
The Monge optimal transport barycenter problem
Andrew D. Lipnick, Esteban G. Tabak, Giulio Trigila +3
A novel methodology is developed for the solution of the data-driven Monge optimal transport barycenter problem, where the pushforward condition is formulated in terms of the stati…
Data-driven continuation of patterns and their bifurcations
Wenjun Zhao, Samuel Maffa, Björn Sandstede
Patterns and nonlinear waves, such as spots, stripes, and rotating spirals, arise prominently in many natural processes and in reaction-diffusion models. Our goal is to compute bou…
A memory-efficient neural ODE framework based on high-level adjoint differentiation
Hong Zhang, Wenjun Zhao
Neural ordinary differential equations (neural ODEs) have emerged as a novel network architecture that bridges dynamical systems and deep learning. However, the gradient obtained w…
Distributional barycenter problem through data-driven flows
Esteban G. Tabak, Giulio Trigila, Wenjun Zhao
A new method is proposed for the solution of the data-driven optimal transport barycenter problem and of the more general distributional barycenter problem that the article introdu…
Data Driven Conditional Optimal Transport
Esteban G. Tabak, Giulio Trigila, Wenjun Zhao
A data driven procedure is developed to compute the optimal map between two conditional probabilities and depending on a set of covari…