3 papers
cs.LG2026
Generative Modeling by Value-Driven Transport
Pablo Moreno-Muñoz, Adrian Müller, Gergely Neu
We propose a new framework for generative modeling based on a discrete-time stochastic control formulation of measure transport. Adapting classic results from control theory, we fo…
stat.ML2024
Decoder ensembling for learned latent geometries
Stas Syrota, Pablo Moreno-Muñoz, Søren Hauberg
Latent space geometry provides a rigorous and empirically valuable framework for interacting with the latent variables of deep generative models. This approach reinterprets Euclide…
cs.LG2024
Gradients of Functions of Large Matrices
Nicholas Krämer, Pablo Moreno-Muñoz, Hrittik Roy +1
Tuning scientific and probabilistic machine learning models for example, partial differential equations, Gaussian processes, or Bayesian neural networks often relies on eva…