7 citations · 31 across the 15 of their papers we have counts for
5 papers · 1 filter
Local-Global MCMC kernels: the best of both worlds
Sergey Samsonov, Evgeny Lagutin, Marylou Gabrié +3
Recent works leveraging learning to enhance sampling have shown promising results, in particular by designing effective non-local moves and global proposals. However, learning accu…
Adaptation of the Independent Metropolis-Hastings Sampler with Normalizing Flow Proposals
James A. Brofos, Marylou Gabrié, Marcus A. Brubaker +1
Markov Chain Monte Carlo (MCMC) methods are a powerful tool for computation with complex probability distributions. However the performance of such methods is critically dependant…
Efficient Bayesian Sampling Using Normalizing Flows to Assist Markov Chain Monte Carlo Methods
Marylou Gabrié, Grant M. Rotskoff, Eric Vanden-Eijnden
Normalizing flows can generate complex target distributions and thus show promise in many applications in Bayesian statistics as an alternative or complement to MCMC for sampling p…
Dual Training of Energy-Based Models with Overparametrized Shallow Neural Networks
Carles Domingo-Enrich, Alberto Bietti, Marylou Gabrié +2
Energy-based models (EBMs) are generative models that are usually trained via maximum likelihood estimation. This approach becomes challenging in generic situations where the train…
On the interplay between data structure and loss function in classification problems
Stéphane d'Ascoli, Marylou Gabrié, Levent Sagun +1
One of the central puzzles in modern machine learning is the ability of heavily overparametrized models to generalize well. Although the low-dimensional structure of typical datase…