24 citations · 76 across the 16 of their papers we have counts for
8 papers · 1 filter
Automatic variational inference with cascading flows
Luca Ambrogioni, Gianluigi Silvestri, Marcel van Gerven
The automation of probabilistic reasoning is one of the primary aims of machine learning. Recently, the confluence of variational inference and deep learning has led to powerful an…
Automatic structured variational inference
Luca Ambrogioni, Kate Lin, Emily Fertig +4
Stochastic variational inference offers an attractive option as a default method for differentiable probabilistic programming. However, the performance of the variational approach…
k-GANs: Ensemble of Generative Models with Semi-Discrete Optimal Transport
Luca Ambrogioni, Umut Güçlü, Marcel van Gerven
Generative adversarial networks (GANs) are the state of the art in generative modeling. Unfortunately, most GAN methods are susceptible to mode collapse, meaning that they tend to…
Perturbative estimation of stochastic gradients
Luca Ambrogioni, Marcel A. J. van Gerven
In this paper we introduce a family of stochastic gradient estimation techniques based of the perturbative expansion around the mean of the sampling distribution. We characterize t…
Wasserstein variational gradient descent: From semi-discrete optimal transport to ensemble variational inference
Luca Ambrogioni, Umut Guclu, Marcel van Gerven
Particle-based variational inference offers a flexible way of approximating complex posterior distributions with a set of particles. In this paper we introduce a new particle-based…
Forward Amortized Inference for Likelihood-Free Variational Marginalization
Luca Ambrogioni, Umut Güçlü, Julia Berezutskaya +5
In this paper, we introduce a new form of amortized variational inference by using the forward KL divergence in a joint-contrastive variational loss. The resulting forward amortize…