121 citations · 560 across the 19 of their papers we have counts for
20 papers
Task-specific experimental design for treatment effect estimation
Bethany Connolly, Kim Moore, Tobias Schwedes +4
Understanding causality should be a core requirement of any attempt to build real impact through AI. Due to the inherent unobservability of counterfactuals, large randomised trials…
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…