3 citations · 8 across the 16 of their papers we have counts for
6 papers · 1 filter
Generative Modeling through Koopman Spectral Analysis: An Operator-Theoretic Perspective
Yuanchao Xu, Fengyi Li, Masahiro Fujisawa +3
We propose Koopman Spectral Wasserstein Gradient Descent (KSWGD), a particle-based generative modeling framework that learns the Langevin generator via Koopman theory and integrate…
Learning Paths for Dynamic Measure Transport: A Control Perspective
Aimee Maurais, Bamdad Hosseini, Youssef Marzouk
We bring a control perspective to the problem of identifying paths of measures for sampling via dynamic measure transport (DMT). We highlight the fact that commonly used paths may…
Localized Diffusion Models
Georg A. Gottwald, Shuigen Liu, Youssef Marzouk +2
Diffusion models are state-of-the-art tools for various generative tasks. Yet training these models involves estimating high-dimensional score functions, a task that in principle s…
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…
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…
Weighted quantization using MMD: From mean field to mean shift via gradient flows
Ayoub Belhadji, Daniel Sharp, Youssef Marzouk
Approximating a probability distribution using a set of particles is a fundamental problem in machine learning and statistics, with applications including clustering and quantizati…