Gaussian mixture models with Wasserstein distance. - Benoit Gaujac; Ilya Feige; David Barber (CoRR, 2018)

Improving latent variable descriptiveness with AutoGen. - Alex Mansbridge; Roberto Fierimonte; Ilya Feige; David Barber (CoRR, 2018)

Invariant-Equivariant Representation Learning for Multi-Class Data. - Ilya Feige (ICML, 2019)

Parenting: Safe Reinforcement Learning from Human Input. - Christopher Frye; Ilya Feige (CoRR, 2019)

Improving latent variable descriptiveness by modelling rather than ad-hoc factors. - Alex Mansbridge; Roberto Fierimonte; Ilya Feige; David Barber (Mach. Learn., 2019)

Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability. - Christopher Frye; Colin Rowat; Ilya Feige (NeurIPS, 2020)

Shapley-based explainability on the data manifold. - Christopher Frye; Damien de Mijolla; Laurence Cowton; Megan Stanley; Ilya Feige (CoRR, 2020)

Learning Deep-Latent Hierarchies by Stacking Wasserstein Autoencoders. - Benoit Gaujac; Ilya Feige; David Barber (CoRR, 2020)

Human-interpretable model explainability on high-dimensional data. - Damien de Mijolla; Christopher Frye; Markus Kunesch; John Mansir; Ilya Feige (CoRR, 2020)

Explainability for fair machine learning. - Tom Begley; Tobias Schwedes; Christopher Frye; Ilya Feige (CoRR, 2020)

Learning to Noise: Application-Agnostic Data Sharing with Local Differential Privacy. - Alex Mansbridge; Gregory Barbour; Davide Piras; Christopher Frye; Ilya Feige; David Barber (CoRR, 2020)

Improving Gaussian mixture latent variable model convergence with Optimal Transport. - Benoit Gaujac; Ilya Feige; David Barber (ACML, 2021)

Shapley explainability on the data manifold. - Christopher Frye; Damien de Mijolla; Tom Begley; Laurence Cowton; Megan Stanley; Ilya Feige (ICLR, 2021)

Learning Disentangled Representations with the Wasserstein Autoencoder. - Benoit Gaujac; Ilya Feige; David Barber (ECML/PKDD (3), 2021)

Task-specific experimental design for treatment effect estimation. - Bethany Connolly; Kim Moore; Tobias Schwedes; Alexander Adam; Gary Willis; Ilya Feige; Christopher Frye (ICML, 2023)