121 citations · 560 across the 19 of their papers we have counts for
6 papers · 1 filter
Explainability for fair machine learning
Tom Begley, Tobias Schwedes, Christopher Frye +1
As the decisions made or influenced by machine learning models increasingly impact our lives, it is crucial to detect, understand, and mitigate unfairness. But even simply determin…
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…
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…
Representation Learning for High-Dimensional Data Collection under Local Differential Privacy
Alex Mansbridge, Gregory Barbour, Davide Piras +4
The collection of individuals' data has become commonplace in many industries. Local differential privacy (LDP) offers a rigorous approach to preserving privacy whereby the individ…
Human-interpretable model explainability on high-dimensional data
Damien de Mijolla, Christopher Frye, Markus Kunesch +2
The importance of explainability in machine learning continues to grow, as both neural-network architectures and the data they model become increasingly complex. Unique challenges…
Shapley explainability on the data manifold
Christopher Frye, Damien de Mijolla, Tom Begley +3
Explainability in AI is crucial for model development, compliance with regulation, and providing operational nuance to predictions. The Shapley framework for explainability attribu…