Massive online teaching to bounded learners

DOI10.1145/2422436.2422438zbMath1362.68121OpenAlexW2042755298MaRDI QIDQ2986853

Brendan Juba, R. Ryan Williams

Publication date: 16 May 2017

Published in: Proceedings of the 4th conference on Innovations in Theoretical Computer Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1145/2422436.2422438

zbMATH Keywords

online learningderandomizationteachingsemantic communication

Mathematics Subject Classification ID

Computational learning theory (68Q32) Computational difficulty of problems (lower bounds, completeness, difficulty of approximation, etc.) (68Q17) Online algorithms; streaming algorithms (68W27)

Related Items (1)

Communication with contextual uncertainty

Cites Work

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• Teachability in computational learning
• Measuring teachability using variants of the teaching dimension
• Teaching a smarter learner.
• Occam's razor
• Pseudorandom generators for space-bounded computation
• On the power of inductive inference from good examples
• A model of interactive teaching
• Learning from different teachers
• On the limits of efficient teachability
• In search of an easy witness: Exponential time vs. probabilistic polynomial time.
• On the complexity of teaching
• On specifying Boolean functions by labelled examples
• Recent Developments in Algorithmic Teaching
• A theory of the learnable
• Teaching Randomized Learners
• Algorithmic Learning Theory
• A theory of goal-oriented communication
• Derandomizing polynomial identity tests means proving circuit lower bounds

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