3 citations · 4 across the 5 of their papers we have counts for
Showing 2025Show all
3 papers · 1 filter
stat.ML2025
Optimal Scheduling of Dynamic Transport
Panos Tsimpos, Zhi Ren, Jakob Zech +1
Flow-based methods for sampling and generative modeling use continuous-time dynamical systems to represent a {transport map} that pushes forward a source measure to a target measur…
cs.LG2025★ 1 cited
Low Stein Discrepancy via Message-Passing Monte Carlo
Nathan Kirk, T. Konstantin Rusch, Jakob Zech +1
Message-Passing Monte Carlo (MPMC) was recently introduced as a novel low-discrepancy sampling approach leveraging tools from geometric deep learning. While originally designed for…
cs.LG2025
Distribution learning via neural differential equations: minimal energy regularization and approximation theory
Youssef Marzouk, Zhi Ren, Jakob Zech
Neural ordinary differential equations (ODEs) provide expressive representations of invertible transport maps that can be used to approximate complex probability distributions, e.g…