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Ilya Feige
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2020 – today
- 2023
- [c6]Bethany Connolly, Kim Moore, Tobias Schwedes, Alexander Adam, Gary Willis, Ilya Feige, Christopher Frye:
Task-specific experimental design for treatment effect estimation. ICML 2023: 6384-6401 - [i12]Bethany Connolly, Kim Moore, Tobias Schwedes, Alexander Adam, Gary Willis, Ilya Feige, Christopher Frye:
Task-specific experimental design for treatment effect estimation. CoRR abs/2306.05484 (2023) - 2021
- [c5]Benoit Gaujac, Ilya Feige, David Barber:
Improving Gaussian mixture latent variable model convergence with Optimal Transport. ACML 2021: 737-752 - [c4]Christopher Frye, Damien de Mijolla, Tom Begley, Laurence Cowton, Megan Stanley, Ilya Feige:
Shapley explainability on the data manifold. ICLR 2021 - [c3]Benoit Gaujac, Ilya Feige, David Barber:
Learning Disentangled Representations with the Wasserstein Autoencoder. ECML/PKDD (3) 2021: 69-84 - 2020
- [c2]Christopher Frye, Colin Rowat, Ilya Feige:
Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability. NeurIPS 2020 - [i11]Christopher Frye, Damien de Mijolla, Laurence Cowton, Megan Stanley, Ilya Feige:
Shapley-based explainability on the data manifold. CoRR abs/2006.01272 (2020) - [i10]Benoit Gaujac, Ilya Feige, David Barber:
Learning disentangled representations with the Wasserstein Autoencoder. CoRR abs/2010.03459 (2020) - [i9]Benoit Gaujac, Ilya Feige, David Barber:
Learning Deep-Latent Hierarchies by Stacking Wasserstein Autoencoders. CoRR abs/2010.03467 (2020) - [i8]Damien de Mijolla, Christopher Frye, Markus Kunesch, John Mansir, Ilya Feige:
Human-interpretable model explainability on high-dimensional data. CoRR abs/2010.07384 (2020) - [i7]Tom Begley, Tobias Schwedes, Christopher Frye, Ilya Feige:
Explainability for fair machine learning. CoRR abs/2010.07389 (2020) - [i6]Alex Mansbridge, Gregory Barbour, Davide Piras
, Christopher Frye, Ilya Feige, David Barber:
Learning to Noise: Application-Agnostic Data Sharing with Local Differential Privacy. CoRR abs/2010.12464 (2020)
2010 – 2019
- 2019
- [j1]Alex Mansbridge
, Roberto Fierimonte, Ilya Feige, David Barber:
Improving latent variable descriptiveness by modelling rather than ad-hoc factors. Mach. Learn. 108(8-9): 1601-1611 (2019) - [c1]Ilya Feige:
Invariant-Equivariant Representation Learning for Multi-Class Data. ICML 2019: 1882-1891 - [i5]Ilya Feige:
Invariant-equivariant representation learning for multi-class data. CoRR abs/1902.03251 (2019) - [i4]Christopher Frye, Ilya Feige:
Parenting: Safe Reinforcement Learning from Human Input. CoRR abs/1902.06766 (2019) - [i3]Christopher Frye, Ilya Feige, Colin Rowat:
Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability. CoRR abs/1910.06358 (2019) - 2018
- [i2]Benoit Gaujac, Ilya Feige, David Barber:
Gaussian mixture models with Wasserstein distance. CoRR abs/1806.04465 (2018) - [i1]Alex Mansbridge, Roberto Fierimonte, Ilya Feige, David Barber:
Improving latent variable descriptiveness with AutoGen. CoRR abs/1806.04480 (2018)
Coauthor Index
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