Lecture Notes in Computer Science - Algorithmic Learning Theory
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Published By Springer International Publishing

9783319244853, 9783319244860

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Two Problems for Sophistication

Lecture Notes in Computer Science - Algorithmic Learning Theory ◽

10.1007/978-3-319-24486-0_25 ◽

2015 ◽

pp. 379-394

Author(s):

Peter Bloem ◽

Steven de Rooij ◽

Pieter Adriaans

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Hierarchical Design of Fast Minimum Disagreement Algorithms

Lecture Notes in Computer Science - Algorithmic Learning Theory ◽

10.1007/978-3-319-24486-0_9 ◽

2015 ◽

pp. 134-148

Author(s):

Malte Darnstädt ◽

Christoph Ries ◽

Hans Ulrich Simon

Keyword(s):

Hierarchical Design

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Information Preserving Dimensionality Reduction

Lecture Notes in Computer Science - Algorithmic Learning Theory ◽

10.1007/978-3-319-24486-0_16 ◽

2015 ◽

pp. 239-253

Author(s):

Shrinu Kushagra ◽

Shai Ben-David

Keyword(s):

Dimensionality Reduction

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Online Linear Optimization for Job Scheduling Under Precedence Constraints

Lecture Notes in Computer Science - Algorithmic Learning Theory ◽

10.1007/978-3-319-24486-0_22 ◽

2015 ◽

pp. 332-346 ◽

Cited By ~ 1

Author(s):

Takahiro Fujita ◽

Kohei Hatano ◽

Shuji Kijima ◽

Eiji Takimoto

Keyword(s):

Job Scheduling ◽

Linear Optimization ◽

Precedence Constraints ◽

Online Linear Optimization

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On the Computability of Solomonoff Induction and Knowledge-Seeking

Lecture Notes in Computer Science - Algorithmic Learning Theory ◽

10.1007/978-3-319-24486-0_24 ◽

2015 ◽

pp. 364-378 ◽

Cited By ~ 1

Author(s):

Jan Leike ◽

Marcus Hutter

Keyword(s):

Knowledge Seeking

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Solomonoff Induction Violates Nicod’s Criterion

Lecture Notes in Computer Science - Algorithmic Learning Theory ◽

10.1007/978-3-319-24486-0_23 ◽

2015 ◽

pp. 349-363 ◽

Cited By ~ 1

Author(s):

Jan Leike ◽

Marcus Hutter

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Adaptive Sampling for Incremental Optimization Using Stochastic Gradient Descent

Lecture Notes in Computer Science - Algorithmic Learning Theory ◽

10.1007/978-3-319-24486-0_21 ◽

2015 ◽

pp. 317-331 ◽

Cited By ~ 1

Author(s):

Guillaume Papa ◽

Pascal Bianchi ◽

Stephan Clémençon

Keyword(s):

Adaptive Sampling ◽

Gradient Descent ◽

Stochastic Gradient ◽

Stochastic Gradient Descent

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Learning a Random DFA from Uniform Strings and State Information

Lecture Notes in Computer Science - Algorithmic Learning Theory ◽

10.1007/978-3-319-24486-0_8 ◽

2015 ◽

pp. 119-133

Author(s):

Dana Angluin ◽

Dongqu Chen

Keyword(s):

State Information

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On the Rademacher Complexity of Weighted Automata

Lecture Notes in Computer Science - Algorithmic Learning Theory ◽

10.1007/978-3-319-24486-0_12 ◽

2015 ◽

pp. 179-193 ◽

Cited By ~ 1

Author(s):

Borja Balle ◽

Mehryar Mohri

Keyword(s):

Rademacher Complexity ◽

Weighted Automata

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On the Teaching Complexity of Linear Sets

Lecture Notes in Computer Science - Algorithmic Learning Theory ◽

10.1007/978-3-319-24486-0_7 ◽

2015 ◽

pp. 102-116 ◽

Cited By ~ 1

Author(s):

Ziyuan Gao ◽

Hans Ulrich Simon ◽

Sandra Zilles

Keyword(s):

Linear Sets

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Lecture Notes in Computer Science - Algorithmic Learning TheoryLatest Publications

TOTAL DOCUMENTS

H-INDEX

Published By Springer International Publishing

Two Problems for Sophistication

Hierarchical Design of Fast Minimum Disagreement Algorithms

Information Preserving Dimensionality Reduction

Online Linear Optimization for Job Scheduling Under Precedence Constraints

On the Computability of Solomonoff Induction and Knowledge-Seeking

Solomonoff Induction Violates Nicod’s Criterion

Adaptive Sampling for Incremental Optimization Using Stochastic Gradient Descent

Learning a Random DFA from Uniform Strings and State Information

On the Rademacher Complexity of Weighted Automata

On the Teaching Complexity of Linear Sets

Lecture Notes in Computer Science - Algorithmic Learning Theory
Latest Publications