- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
1573-0565
- Extent
-
Online-Ressource
- Language
-
Englisch
- Notes
-
online resource.
- Bibliographic citation
-
Responsible model deployment via model-agnostic uncertainty learning ; day:18 ; month:10 ; year:2022 ; pages:1-32
Machine learning ; (18.10.2022), 1-32
- Creator
-
Lahoti, Preethi
Gummadi, Krishna
Weikum, Gerhard
- Contributor
-
SpringerLink (Online service)
- DOI
-
10.1007/s10994-022-06248-y
- URN
-
urn:nbn:de:101:1-2023010321204664469349
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:29 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Lahoti, Preethi
- Gummadi, Krishna
- Weikum, Gerhard
- SpringerLink (Online service)
Other Objects (12)
Transformation of technology-specific deployment models into technology-agnostic deployment models
Model-Agnostic Private Learning
Deployment-technology-agnostic management of running applications
Model-agnostic variable importance for predictive uncertainty: an entropy-based approach
Model-agnostic Interpretable Machine Learning
Model-agnostic interpretable machine learning
Is precipitation responsible for the most hydrological model uncertainty?
Structural models for NBN deployment
TS-MULE : Local Interpretable Model-Agnostic Explanations for Time Series Forecast Models
Boosting mono-jet searches with model-agnostic machine learning
Boosting mono-jet searches with model-agnostic machine learning
Agnostic Explanation of Model Change based on Feature Importance
Transformation of technology-specific deployment models into technology-agnostic deployment models
Model-Agnostic Private Learning
Deployment-technology-agnostic management of running applications
Model-agnostic variable importance for predictive uncertainty: an entropy-based approach
Model-agnostic Interpretable Machine Learning
Model-agnostic interpretable machine learning
Is precipitation responsible for the most hydrological model uncertainty?
Structural models for NBN deployment
TS-MULE : Local Interpretable Model-Agnostic Explanations for Time Series Forecast Models
Boosting mono-jet searches with model-agnostic machine learning
Boosting mono-jet searches with model-agnostic machine learning
Agnostic Explanation of Model Change based on Feature Importance
Transformation of technology-specific deployment models into technology-agnostic deployment models
Model-Agnostic Private Learning
Deployment-technology-agnostic management of running applications
Model-agnostic variable importance for predictive uncertainty: an entropy-based approach
Model-agnostic Interpretable Machine Learning
Model-agnostic interpretable machine learning
Is precipitation responsible for the most hydrological model uncertainty?
Structural models for NBN deployment
TS-MULE : Local Interpretable Model-Agnostic Explanations for Time Series Forecast Models
Boosting mono-jet searches with model-agnostic machine learning
Boosting mono-jet searches with model-agnostic machine learning