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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…