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ORCID19. ALT 2008: Budapest, Hungary
- Yoav Freund, László Györfi, György Turán, Thomas Zeugmann:
Algorithmic Learning Theory, 19th International Conference, ALT 2008, Budapest, Hungary, October 13-16, 2008. Proceedings. Lecture Notes in Computer Science 5254, Springer 2008, ISBN 978-3-540-87986-2
Invited Papers
- Imre Csiszár:
On Iterative Algorithms with an Information Geometry Background. 1 - Daniel A. Keim, Florian Mansmann, Daniela Oelke, Hartmut Ziegler:
Visual Analytics: Combining Automated Discovery with Interactive Visualizations. 2 - László Lovász:
Some Mathematics behind Graph Property Testing. 3 - Heikki Mannila:
Finding Total and Partial Orders from Data for Seriation. 4 - Tom M. Mitchell:
Computational Models of Neural Representations in the Human Brain. 5-6
Statistical Learning
- Shivani Agarwal:
Generalization Bounds for Some Ordinal Regression Algorithms. 7-21 - Stéphan Clémençon, Nicolas Vayatis:
Approximation of the Optimal ROC Curve and a Tree-Based Ranking Algorithm. 22-37 - Corinna Cortes, Mehryar Mohri, Michael Riley, Afshin Rostamizadeh:
Sample Selection Bias Correction Theory. 38-53 - Mark Herbster:
Exploiting Cluster-Structure to Predict the Labeling of a Graph. 54-69 - Andreas Maurer, Massimiliano Pontil:
A Uniform Lower Error Bound for Half-Space Learning. 70-78 - Andreas Maurer, Massimiliano Pontil:
Generalization Bounds for K-Dimensional Coding Schemes in Hilbert Spaces. 79-91 - Ohad Shamir, Sivan Sabato
, Naftali Tishby:
Learning and Generalization with the Information Bottleneck. 92-107
Probability and Stochastic Processes
- László Györfi, István Vajda:
Growth Optimal Investment with Transaction Costs. 108-122 - Ronald Ortner:
Online Regret Bounds for Markov Decision Processes with Deterministic Transitions. 123-137 - Alexey V. Chernov, Alexander Shen, Nikolai K. Vereshchagin, Vladimir Vovk:
On-Line Probability, Complexity and Randomness. 138-153 - Vladimir Vovk, Alexander Shen:
Prequential Randomness. 154-168 - Daniil Ryabko:
Some Sufficient Conditions on an Arbitrary Class of Stochastic Processes for the Existence of a Predictor. 169-182 - Arthur Gretton, László Györfi:
Nonparametric Independence Tests: Space Partitioning and Kernel Approaches. 183-198
Boosting and Experts
- Alexey V. Chernov, Yuri Kalnishkan, Fedor Zhdanov, Vladimir Vovk:
Supermartingales in Prediction with Expert Advice. 199-213 - Mikhail Dashevskiy:
Aggregating Algorithm for a Space of Analytic Functions. 214-226 - Jun-ichi Moribe, Kohei Hatano, Eiji Takimoto, Masayuki Takeda:
Smooth Boosting for Margin-Based Ranking. 227-239 - Indraneel Mukherjee, Robert E. Schapire:
Learning with Continuous Experts Using Drifting Games. 240-255 - Manfred K. Warmuth, Karen A. Glocer, S. V. N. Vishwanathan:
Entropy Regularized LPBoost. 256-271
Active Learning and Queries
- Dana Angluin, James Aspnes, Lev Reyzin:
Optimally Learning Social Networks with Activations and Suppressions. 272-286 - András Antos, Varun Grover, Csaba Szepesvári:
Active Learning in Multi-armed Bandits. 287-302 - Marta Arias, José L. Balcázar:
Query Learning and Certificates in Lattices. 303-315 - Maria-Florina Balcan, Avrim Blum:
Clustering with Interactive Feedback. 316-328 - Gábor Bartók, Csaba Szepesvári, Sandra Zilles:
Active Learning of Group-Structured Environments. 329-343 - Shlomo Hoory, Oded Margalit:
Finding the Rare Cube. 344-358
Inductive Inference
- Leonor Becerra-Bonache, John Case, Sanjay Jain, Frank Stephan:
Iterative Learning of Simple External Contextual Languages. 359-373 - Matthew de Brecht, Akihiro Yamamoto:
Topological Properties of Concept Spaces. 374-388 - John Case, Timo Kötzing:
Dynamically Delayed Postdictive Completeness and Consistency in Learning. 389-403 - John Case, Timo Kötzing:
Dynamic Modeling in Inductive Inference. 404-418 - John Case, Samuel E. Moelius:
Optimal Language Learning. 419-433 - Sanjay Jain, Frank Stephan:
Numberings Optimal for Learning. 434-448 - Steffen Lange, Samuel E. Moelius, Sandra Zilles:
Learning with Temporary Memory. 449-463
Erratum
- Jittat Fakcharoenphol, Boonserm Kijsirikul:
Erratum: Constructing Multiclass Learners from Binary Learners: A Simple Black-Box Analysis of the Generalization Errors. 464-466
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