Ilya Feige

Publication Activity (10 Years)

Years Active: 2018-2023
Publications (10 Years): 19

Top Topics

Gaussian Mixture

Experimental Designs

Top Venues

Publications

Bethany Connolly, Kim Moore, Tobias Schwedes, Alexander Adam, Gary Willis, Ilya Feige, Christopher Frye
Task-specific experimental design for treatment effect estimation. CoRR (2023)
Bethany Connolly, Kim Moore, Tobias Schwedes, Alexander Adam, Gary Willis, Ilya Feige, Christopher Frye
Task-specific experimental design for treatment effect estimation. ICML (2023)
Benoit Gaujac, Ilya Feige, David Barber
Improving Gaussian mixture latent variable model convergence with Optimal Transport. ACML (2021)
Benoit Gaujac, Ilya Feige, David Barber
Learning Disentangled Representations with the Wasserstein Autoencoder. ECML/PKDD (3) (2021)
Christopher Frye, Damien de Mijolla, Tom Begley, Laurence Cowton, Megan Stanley, Ilya Feige
Shapley explainability on the data manifold. ICLR (2021)
Benoit Gaujac, Ilya Feige, David Barber
Learning Deep-Latent Hierarchies by Stacking Wasserstein Autoencoders. CoRR (2020)
Alex Mansbridge, Gregory Barbour, Davide Piras, Christopher Frye, Ilya Feige, David Barber
Learning to Noise: Application-Agnostic Data Sharing with Local Differential Privacy. CoRR (2020)
Christopher Frye, Colin Rowat, Ilya Feige
Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability. NeurIPS (2020)
Tom Begley, Tobias Schwedes, Christopher Frye, Ilya Feige
Explainability for fair machine learning. CoRR (2020)
Benoit Gaujac, Ilya Feige, David Barber
Learning disentangled representations with the Wasserstein Autoencoder. CoRR (2020)
Damien de Mijolla, Christopher Frye, Markus Kunesch, John Mansir, Ilya Feige
Human-interpretable model explainability on high-dimensional data. CoRR (2020)
Christopher Frye, Damien de Mijolla, Laurence Cowton, Megan Stanley, Ilya Feige
Shapley-based explainability on the data manifold. CoRR (2020)
Ilya Feige
Invariant-equivariant representation learning for multi-class data. CoRR (2019)
Ilya Feige
Invariant-Equivariant Representation Learning for Multi-Class Data. ICML (2019)
Christopher Frye, Ilya Feige
Parenting: Safe Reinforcement Learning from Human Input. CoRR (2019)
Alex Mansbridge, Roberto Fierimonte, Ilya Feige, David Barber
Improving latent variable descriptiveness by modelling rather than ad-hoc factors. Mach. Learn. 108 (8-9) (2019)
Christopher Frye, Ilya Feige, Colin Rowat
Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability. CoRR (2019)
Benoit Gaujac, Ilya Feige, David Barber
Gaussian mixture models with Wasserstein distance. CoRR (2018)
Alex Mansbridge, Roberto Fierimonte, Ilya Feige, David Barber
Improving latent variable descriptiveness with AutoGen. CoRR (2018)