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Stochastic Localization via Iterative Posterior Sampling
Louis Grenioux, Maxence Noble, Marylou Gabrié +1
Building upon score-based learning, new interest in stochastic localization techniques has recently emerged. In these models, one seeks to noise a sample from the data distribution…
Annealing in variational inference mitigates mode collapse: A theoretical study on Gaussian mixtures
Luigi Fogliani, Bruno Loureiro, Marylou Gabrié
Mode collapse, the failure to capture one or more modes when targetting a multimodal distribution, is a central challenge in modern variational inference. In this work, we provide…
Boltzmann generators for amorphous particle systems
Louis Grenioux, Leonardo Galliano, Ludovic Berthier +2
Sampling configurations in thermodynamic equilibrium is a long-standing challenge in statistical physics. Boltzmann generators address this problem by employing generative models t…
Learned Reference-based Diffusion Sampling for multi-modal distributions
Maxence Noble, Louis Grenioux, Marylou Gabrié +1
Over the past few years, several approaches utilizing score-based diffusion have been proposed to sample from probability distributions, that is without having access to exact samp…
Improving the evaluation of samplers on multi-modal targets
Louis Grenioux, Maxence Noble, Marylou Gabrié
Addressing multi-modality constitutes one of the major challenges of sampling. In this reflection paper, we advocate for a more systematic evaluation of samplers towards two source…