45 citations · 126 across the 10 of their papers we have counts for
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stat.ML2020
Learning Deep-Latent Hierarchies by Stacking Wasserstein Autoencoders
Benoit Gaujac, Ilya Feige, David Barber
Probabilistic models with hierarchical-latent-variable structures provide state-of-the-art results amongst non-autoregressive, unsupervised density-based models. However, the most…
stat.ML2020
Learning disentangled representations with the Wasserstein Autoencoder
Benoit Gaujac, Ilya Feige, David Barber
Disentangled representation learning has undoubtedly benefited from objective function surgery. However, a delicate balancing act of tuning is still required in order to trade off…
cs.LG2020
Private Machine Learning via Randomised Response
David Barber
We introduce a general learning framework for private machine learning based on randomised response. Our assumption is that all actors are potentially adversarial and as such we tr…