Algorithmic Learning Theory
for ALT 2014 in
Theoretical Computer Science
Algorithmic Learning Theoryfor ALT 2014 in Theoretical Computer Science |
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appeared in
The Special Issue on Algorithmic Learning Theory has been edited by Peter Auer, Alexander Clark, and Thomas Zeugmann. |
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Peter Auer, Alexander Clark, and Thomas Zeugmann Guest Editors' Foreword | pp. 1–3 |
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Timo Kötzing
and Raphaela Palenta.
A map of update constraints in inductive inference , Abstract. | pp. 4–24 |
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Sanjay Jain
and
Efim Kinber.
Parallel learning of automatic classes of languages, Abstract. | pp. 25–44 |
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Hasan Abasi, Ali Z. Abdi, and
Nader H. Bshouty.
Learning boolean halfspaces with small weights from membership queries, Abstract. | pp. 45–56 |
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Dana Angluin
and
Dana Fisman.
Learning regular omega languages, Abstract. | pp. 57–72 |
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Marcus Hutter.
Extreme state aggregation beyond Markov decision processes, Abstract. | pp. 73–91 |
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Nir Ailon,
Kohei Hatano,
and
Eiji Takimoto.
Bandit online optimization over the permutahedron, Abstract. | pp. 92–108 |
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Andreas Maurer.
A chain rule for the expected suprema of Gaussian processes. Abstract. | pp. 109–122 |
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