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Feige, Ilya
33  Ergebnisse:
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1

Large-scale calibration and simulation of COVID-19 epidemio..:

Groves-Kirkby, Nick ; Wakeman, Ewan ; Patel, Seema...
Epidemics.  42 (2023)  - p. 100662 , 2023
Link: https://doi.org/10.1016/..
 
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2

Learning Disentangled Representations with the Wasserstein ..:

, In: Machine Learning and Knowledge Discovery in Databases. Research Track; Lecture Notes in Computer Science,
Gaujac, Benoit ; Feige, Ilya ; Barber, David - p. 69-84 , 2021
Link: https://doi.org/10.1007/..
 
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3

Improving latent variable descriptiveness by modelling rath..:

Mansbridge, Alex ; Fierimonte, Roberto ; Feige, Ilya.
Machine Learning.  108 (2019)  8-9 - p. 1601-1611 , 2019
Link: https://doi.org/10.1007/..
 
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4

binary junipr: An Interpretable Probabilistic Model for Dis..:

Andreassen, Anders ; Feige, Ilya ; Frye, Christopher.
Physical Review Letters.  123 (2019)  18 - p. , 2019
Link: https://doi.org/10.1103/..
 
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5

JUNIPR: a framework for unsupervised machine learning in pa..:

Andreassen, Anders ; Feige, Ilya ; Frye, Christopher.
The European Physical Journal C.  79 (2019)  2 - p. , 2019
Link: https://doi.org/10.1140/..
 
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6

A complete basis of helicity operators for subleading facto..:

Feige, Ilya ; Kolodrubetz, Daniel W. ; Moult, Ian.
Journal of High Energy Physics.  2017 (2017)  11 - p. , 2017
Link: https://doi.org/10.1007/..
 
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7

Precision Jet Substructure from Boosted Event Shapes:

Feige, Ilya ; Schwartz, Matthew D. ; Stewart, Iain W..
Physical Review Letters.  109 (2012)  9 - p. , 2012
Link: https://doi.org/10.1103/..
 
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8

Task-specific experimental design for treatment effect esti..:

Connolly, Bethany ; Moore, Kim ; Schwedes, Tobias...
http://arxiv.org/abs/2306.05484.  , 2023
Link: http://arxiv.org/abs/230..
 
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9

Shapley explainability on the data manifold:

Frye, Christopher ; de Mijolla, Damien ; Begley, Tom...
http://arxiv.org/abs/2006.01272.  , 2020
Link: http://arxiv.org/abs/200..
 
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10

Learning disentangled representations with the Wasserstein ..:

Gaujac, Benoit ; Feige, Ilya ; Barber, David
http://arxiv.org/abs/2010.03459.  , 2020
Link: http://arxiv.org/abs/201..
 
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11

Learning Deep-Latent Hierarchies by Stacking Wasserstein Au..:

Gaujac, Benoit ; Feige, Ilya ; Barber, David
http://arxiv.org/abs/2010.03467.  , 2020
Link: http://arxiv.org/abs/201..
 
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12

Explainability for fair machine learning:

Begley, Tom ; Schwedes, Tobias ; Frye, Christopher.
http://arxiv.org/abs/2010.07389.  , 2020
Link: http://arxiv.org/abs/201..
 
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13

Human-interpretable model explainability on high-dimensiona..:

de Mijolla, Damien ; Frye, Christopher ; Kunesch, Markus..
http://arxiv.org/abs/2010.07384.  , 2020
Link: http://arxiv.org/abs/201..
 
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14

Representation Learning for High-Dimensional Data Collectio..:

Mansbridge, Alex ; Barbour, Gregory ; Piras, Davide...
http://arxiv.org/abs/2010.12464.  , 2020
Link: http://arxiv.org/abs/201..
 
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15

Asymmetric Shapley values: incorporating causal knowledge i..:

Frye, Christopher ; Rowat, Colin ; Feige, Ilya
http://arxiv.org/abs/1910.06358.  , 2019
Link: http://arxiv.org/abs/191..
 
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