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stat.ML2025
Sample and Map from a Single Convex Potential: Generation using Conjugate Moment Measures
Nina Vesseron, Louis Béthune, Marco Cuturi
The canonical approach in generative modeling is to split model fitting into two blocks: define first how to sample noise (e.g. Gaussian) and choose next what to do with it (e.g. u…
stat.ML2025
Composition and Control with Distilled Energy Diffusion Models and Sequential Monte Carlo
James Thornton, Louis Bethune, Ruixiang Zhang +3
Diffusion models may be formulated as a time-indexed sequence of energy-based models, where the score corresponds to the negative gradient of an energy function. As opposed to lear…
stat.ML2025
Multivariate Conformal Prediction using Optimal Transport
Michal Klein, Louis Bethune, Eugene Ndiaye +1
Conformal prediction (CP) quantifies the uncertainty of machine learning models by constructing sets of plausible outputs. These sets are constructed by leveraging a so-called conf…