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Keyword

PAC learnability

Description: PAC learnability is a machine-learning framework describing whether an algorithm can probably find an approximately accurate hypothesis from sampled data.
Wikidata ID: wiki/Q458526
OpenAlex ID: keywords/pac-learnability
Learning Decision Lists
1987 · Ronald L. Rivest · Machine Learning · 905
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Scale-sensitive dimensions, uniform convergence, and learnability
1997 · Noga Alon, Shai Ben-David, et al. · Journal of the ACM · 340
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Supervised versus multiple instance learning
2005 · Soumya Ray, Mark Craven · International Conference on Machine Learning · 222
Linguistic Nativism and the Poverty of the Stimulus
2011 · Alexander Clark, Shalom Lappin · Wiley eBooks · 219
Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited
2015 · Stephen H. Muggleton, Dianhuan Lin, et al. · Machine Learning · 184
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Learning Conjunctive Concepts in Structural Domains
1989 · David Haussler · Machine Learning · 171
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Tracking Drifting Concepts By Minimizing Disagreements
1994 · David P. Helmbold, Philip M. Long · Machine Learning · 161
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Contextual Decision Processes with Low Bellman Rank are PAC-Learnable
2016 · Jiang, Nan, Krishnamurthy, Akshay, et al. · arXiv (Cornell University) · 144
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First-order jk-clausal theories are PAC-learnable
1994 · Luc De Raedt, Sašo Džeroski · Artificial Intelligence · 141
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On the Fourier spectrum of monotone functions
1995 · Nader H. Bshouty, Christino Tamon · ACM Symposium on Theory of Computing (STOC) · 126
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PAC-learnability of determinate logic programs
1992 · Sašo Džeroski, Stephen Muggleton, et al. · 118
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Lower Bounds on Learning Decision Lists and Trees
1996 · Thomas Hancock, Tao Jiang, et al. · Information and Computation · 98
Characterizations of Learnability for Classes of {0, ..., n)-Valued Functions
1995 · S. Bendavid, N. Cesabianchi, et al. · Journal of Computer and System Sciences · 92
On the Quantum versus Classical Learnability of Discrete Distributions
2021 · Ryan Sweke, Jean-Pierre Seifert, et al. · Quantum · 86
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PAC-learnability of Probabilistic Deterministic Finite State Automata
2004 · Alexander Clark, Franck Thollard · Research Portal (King's College London) · 80
Parikh Images of Grammars: Complexity and Applications
2010 · Eryk Kopczynski, Anthony Widjaja To · Proceedings - Symposium on Logic in Computer Science · 78
Learning linear threshold functions in the presence of classification noise
1994 · Tom Bylander · Annual Conference on Computational Learning Theory (COLT) · 77
A result of Vapnik with applications
1993 · Martin Anthony, John Shawe-Taylor · Discrete Applied Mathematics · 76
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A machine discovery from amino acid sequences by decision trees over regular patterns
1993 · Setsuo Arikawa, Satoru Miyano, et al. · New Generation Computing · 72
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Learning Behaviors of Automata from Multiplicity and Equivalence Queries
1996 · Francesco Bergadano, Stefano Varricchio · SIAM Journal on Computing · 64
Deep Learning for Multi-Facility Location Mechanism Design
2018 · Noah Golowich, Harikrishna Narasimhan, et al. · International Joint Conference on Artificial Intelligence · 62
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Computational Learning Theory
1995 · Alessandro Moschitti · Lecture notes in computer science · 62
RHMD
2017 · Khaled N. Khasawneh, Nael Abu-Ghazaleh, et al. · Proceedings of the ... annual International Symposium on Microarchitecture/Proceedings of the annual International Symposium on Microarchitecture · 60
l 1 -regularized neural networks are improperly learnable in polynomial time
2016 · Yuchen Zhang, Jason D. Lee, et al. · 57
Learning noisy perceptrons by a perceptron in polynomial time
2002 · E. Cohen · Annual Symposium on Foundations of Computer Science · 54
Works count: 438
Citation count: 6,287
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