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pro vyhledávání: '"Haug, Johannes"'
As complex machine learning models are increasingly used in sensitive applications like banking, trading or credit scoring, there is a growing demand for reliable explanation mechanisms. Local feature attribution methods have become a popular techniq
Externí odkaz:
http://arxiv.org/abs/2209.02764
Due to the unspecified and dynamic nature of data streams, online machine learning requires powerful and flexible solutions. However, evaluating online machine learning methods under realistic conditions is difficult. Existing work therefore often dr
Externí odkaz:
http://arxiv.org/abs/2204.13625
Data streams are ubiquitous in modern business and society. In practice, data streams may evolve over time and cannot be stored indefinitely. Effective and transparent machine learning on data streams is thus often challenging. Hoeffding Trees have e
Externí odkaz:
http://arxiv.org/abs/2203.16181
Autor:
Borisov, Vadim, Leemann, Tobias, Seßler, Kathrin, Haug, Johannes, Pawelczyk, Martin, Kasneci, Gjergji
Heterogeneous tabular data are the most commonly used form of data and are essential for numerous critical and computationally demanding applications. On homogeneous data sets, deep neural networks have repeatedly shown excellent performance and have
Externí odkaz:
http://arxiv.org/abs/2110.01889
High-performing predictive models, such as neural nets, usually operate as black boxes, which raises serious concerns about their interpretability. Local feature attribution methods help to explain black box models and are therefore a powerful tool f
Externí odkaz:
http://arxiv.org/abs/2101.00905
Autor:
Haug, Johannes, Kasneci, Gjergji
Data distributions in streaming environments are usually not stationary. In order to maintain a high predictive quality at all times, online learning models need to adapt to distributional changes, which are known as concept drift. The timely and rob
Externí odkaz:
http://arxiv.org/abs/2010.09388
Feature selection can be a crucial factor in obtaining robust and accurate predictions. Online feature selection models, however, operate under considerable restrictions; they need to efficiently extract salient input features based on a bounded set
Externí odkaz:
http://arxiv.org/abs/2006.10398
Counterfactual explanations can be obtained by identifying the smallest change made to a feature vector to qualitatively influence a prediction; for example, from 'loan rejected' to 'awarded' or from 'high risk of cardiovascular disease' to 'low risk
Externí odkaz:
http://arxiv.org/abs/1910.09398
Autor:
Borisov, Vadim, Leemann, Tobias, Sebler, Kathrin, Haug, Johannes, Pawelczyk, Martin, Kasneci, Gjergji
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems; 2024, Vol. 35 Issue: 6 p7499-7519, 21p
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