The Computational Complexity of Understanding Binary Classifier Decisions
von Stephan Wäldchen, Jan Macdonald, Sascha Hauch, Gitta Kutyniok
Jahr:
2021
Publikation:
JAIR, Vol. 70
Abstrakt:
For a d-ary Boolean function Φ: {0, 1}d → {0, 1} and an assignment to its variables x = (x1, x2, . . . , xd) we consider the problem of finding those subsets of the variables that are sufficient to determine the function value with a given probability δ. This is motivated by the task of interpreting predictions of binary classifiers described as Boolean circuits, which can be seen as special cases of neural networks.
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Brief introduction of the dida co-author(s) and relevance for dida's ML developments.
About the Co-Author
During his studies in mathematics (TU Berlin) Jan focussed on applied topics in optimization, functional analysis, and image processing. His doctoral studies (TU Berlin) explored the interplay between theoretical and empirical research on neural networks. This resulted in his PhD thesis investigating the reliability of deep learning for imaging and computer vision tasks in terms of interpretability, robustness, and accuracy. At dida he works as a Machine Learning Researcher at the interface of scientific research and software development.