Ilya Feige

According to our database, Ilya Feige authored at least 15 papers between 2018 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2023

Task-specific experimental design for treatment effect estimation.

Bethany Connolly, Kim Moore, Tobias Schwedes, Alexander Adam, Gary Willis, Ilya Feige, Christopher Frye

Proceedings of the International Conference on Machine Learning, 2023

2021

Learning Disentangled Representations with the Wasserstein Autoencoder.

Benoit Gaujac, Ilya Feige, David Barber

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2021

Shapley explainability on the data manifold.

Christopher Frye, Damien de Mijolla, Tom Begley, Laurence Cowton, Megan Stanley, Ilya Feige

Proceedings of the 9th International Conference on Learning Representations, 2021

Improving Gaussian mixture latent variable model convergence with Optimal Transport.

Benoit Gaujac, Ilya Feige, David Barber

Proceedings of the Asian Conference on Machine Learning, 2021

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

Explainability for fair machine learning.

Tom Begley, Tobias Schwedes, Christopher Frye, Ilya Feige

CoRR, 2020

Human-interpretable model explainability on high-dimensional data.

Damien de Mijolla, Christopher Frye, Markus Kunesch, John Mansir, Ilya Feige

CoRR, 2020

Learning Deep-Latent Hierarchies by Stacking Wasserstein Autoencoders.

Benoit Gaujac, Ilya Feige, David Barber

CoRR, 2020

Shapley-based explainability on the data manifold.

Christopher Frye, Damien de Mijolla, Laurence Cowton, Megan Stanley, Ilya Feige

CoRR, 2020

Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability.

Christopher Frye, Colin Rowat, Ilya Feige

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Improving latent variable descriptiveness by modelling rather than ad-hoc factors.

Alex Mansbridge, Roberto Fierimonte, Ilya Feige, David Barber

Mach. Learn., 2019

Parenting: Safe Reinforcement Learning from Human Input.

Christopher Frye, Ilya Feige

CoRR, 2019

Invariant-Equivariant Representation Learning for Multi-Class Data.

Ilya Feige

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Improving latent variable descriptiveness with AutoGen.

Alex Mansbridge, Roberto Fierimonte, Ilya Feige, David Barber

CoRR, 2018

Gaussian mixture models with Wasserstein distance.

Benoit Gaujac, Ilya Feige, David Barber

CoRR, 2018