335 citations · 1.6k across the 27 of their papers we have counts for
7 papers · 1 filter
DIVA: Domain Invariant Variational Autoencoders
Maximilian Ilse, Jakub M. Tomczak, Christos Louizos +1
We consider the problem of domain generalization, namely, how to learn representations given data from a set of domains that generalize to data from a previously unseen domain. We…
Learning Sparse Neural Networks through Regularization
Christos Louizos, Max Welling, Diederik P. Kingma
We propose a practical method for norm regularization for neural networks: pruning the network during training by encouraging weights to become exactly zero. Such regularizat…
Causal Effect Inference with Deep Latent-Variable Models
Christos Louizos, Uri Shalit, Joris Mooij +3
Learning individual-level causal effects from observational data, such as inferring the most effective medication for a specific patient, is a problem of growing importance for pol…
Bayesian Compression for Deep Learning
Christos Louizos, Karen Ullrich, Max Welling
Compression and computational efficiency in deep learning have become a problem of great significance. In this work, we argue that the most principled and effective way to attack t…
Multiplicative Normalizing Flows for Variational Bayesian Neural Networks
Christos Louizos, Max Welling
We reinterpret multiplicative noise in neural networks as auxiliary random variables that augment the approximate posterior in a variational setting for Bayesian neural networks. W…
Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors
Christos Louizos, Max Welling
We introduce a variational Bayesian neural network where the parameters are governed via a probability distribution on random matrices. Specifically, we employ a matrix variate Gau…
The Variational Fair Autoencoder
Christos Louizos, Kevin Swersky, Yujia Li +2
We investigate the problem of learning representations that are invariant to certain nuisance or sensitive factors of variation in the data while retaining as much of the remaining…