Ilya Feige - RankMe

19 papers A* 4C 1Journal 13Unranked 1

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