246 citations · 492 across the 24 of their papers we have counts for
3 papers · 2 filters
Statistical Guarantees for Variational Autoencoders using PAC-Bayesian Theory
Sokhna Diarra Mbacke, Florence Clerc, Pascal Germain
Since their inception, Variational Autoencoders (VAEs) have become central in machine learning. Despite their widespread use, numerous questions regarding their theoretical propert…
Invariant Causal Set Covering Machines
Thibaud Godon, Baptiste Bauvin, Pascal Germain +2
Rule-based models, such as decision trees, appeal to practitioners due to their interpretable nature. However, the learning algorithms that produce such models are often vulnerable…
PAC-Bayesian Generalization Bounds for Adversarial Generative Models
Sokhna Diarra Mbacke, Florence Clerc, Pascal Germain
We extend PAC-Bayesian theory to generative models and develop generalization bounds for models based on the Wasserstein distance and the total variation distance. Our first result…