Ilya Feige
Ilya Feige
Director of AI, Faculty
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Cited by
Cited by
Precision jet substructure from boosted event shapes
I Feige, MD Schwartz, IW Stewart, J Thaler
Physical review letters 109 (9), 092001, 2012
Hard-soft-collinear factorization to all orders
I Feige, MD Schwartz
Physical Review D 90 (10), 105020, 2014
JUNIPR: a framework for unsupervised machine learning in particle physics
A Andreassen, I Feige, C Frye, MD Schwartz
The European Physical Journal C 79 (2), 1-24, 2019
A complete basis of helicity operators for subleading factorization
I Feige, DW Kolodrubetz, I Moult, IW Stewart
Journal of High Energy Physics 2017 (11), 1-109, 2017
An on-shell approach to factorization
I Feige, MD Schwartz
Physical Review D 88 (6), 065021, 2013
Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability
C Frye, C Rowat, I Feige
Advances in Neural Information Processing Systems 33, 2020
Shapley explainability on the data manifold
C Frye, D de Mijolla, T Begley, L Cowton, M Stanley, I Feige
arXiv preprint arXiv:2006.01272, 2020
binary junipr: An Interpretable Probabilistic Model for Discrimination
A Andreassen, I Feige, C Frye, MD Schwartz
Physical review letters 123 (18), 182001, 2019
Removing phase-space restrictions in factorized cross sections
I Feige, MD Schwartz, K Yan
Physical Review D 91 (9), 094027, 2015
Streamlining resummed QCD calculations using Monte Carlo integration
D Farhi, I Feige, M Freytsis, MD Schwartz
Journal of High Energy Physics 2016 (8), 1-34, 2016
Gaussian mixture models with Wasserstein distance
B Gaujac, I Feige, D Barber
arXiv preprint arXiv:1806.04465, 2018
Explainability for fair machine learning
T Begley, T Schwedes, C Frye, I Feige
arXiv preprint arXiv:2010.07389, 2020
Invariant-equivariant representation learning for multi-class data
I Feige
International Conference on Machine Learning, 1882-1891, 2019
Human-interpretable model explainability on high-dimensional data
D de Mijolla, C Frye, M Kunesch, J Mansir, I Feige
arXiv preprint arXiv:2010.07384, 2020
Parenting: Safe reinforcement learning from human input
C Frye, I Feige
arXiv preprint arXiv:1902.06766, 2019
Learning to Noise: Application-Agnostic Data Sharing with Local Differential Privacy
A Mansbridge, G Barbour, D Piras, C Frye, I Feige, D Barber
arXiv preprint arXiv:2010.12464, 2020
Improving latent variable descriptiveness by modelling rather than ad-hoc factors
A Mansbridge, R Fierimonte, I Feige, D Barber
Machine Learning 108 (8), 1601-1611, 2019
Learning disentangled representations with the Wasserstein autoencoder
B Gaujac, I Feige, D Barber
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2021
Learning Deep-Latent Hierarchies by Stacking Wasserstein Autoencoders
B Gaujac, I Feige, D Barber
arXiv preprint arXiv:2010.03467, 2020
Factorization and Precision Calculations in Particle Physics
IEA Feige
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