Record link: 
http://hdl.handle.net/20.500.12708/188263
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Title: 
A Parameterized Theory of PAC Learning
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Citation: 
Brand, C., Ganian, R., & Simonov, K. (2023). A Parameterized Theory of PAC Learning. In B. Williams, Y. Chen, & J. Neville (Eds.), Proceedings of the AAAI Conference on Artificial Intelligence (pp. 6834–6841). AAAI Press. https://doi.org/10.1609/aaai.v37i6.25837
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Publisher DOI: 
10.1609/aaai.v37i6.25837
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Publication Type: 
Inproceedings - Full-Paper Contribution
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Language: 
English
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Authors: 
Brand, Cornelius 
Ganian, Robert 
Simonov, Kirill 
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Organisational Unit: 
E192 - Institut für Logic and Computation 
E192-01 - Forschungsbereich Algorithms and Complexity 
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Published in: 
Proceedings of the AAAI Conference on Artificial Intelligence 
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ISBN: 
978-1-57735-880-0
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Volume: 
37, 6
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Date (published): 
2023
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Event name: 
The 37th AAAI Conference on Artificial Intelligence (AAAI -23) 
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Event date: 
7-Feb-2023 - 14-Feb-2023
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Event place: 
Washington DC, United States of America (the)
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Number of Pages: 
8
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Publisher: 
AAAI Press, Washington DC
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Peer reviewed: 
Yes
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Keywords: 
PAC learning; Parameterized complexity; Sample complexity; Learning Theory; Computational Complexity of Reasoning; Reinforcement Learning Theory
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Research Areas: 
Logic and Computation: 100%
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Science Branch: 
1020 - Informatik: 80%
1010 - Mathematik: 20%
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Appears in Collections:
Conference Paper

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