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ORCID22. ALT 2011: Espoo, Finland
- Jyrki Kivinen
, Csaba Szepesvári, Esko Ukkonen, Thomas Zeugmann:
Algorithmic Learning Theory - 22nd International Conference, ALT 2011, Espoo, Finland, October 5-7, 2011. Proceedings. Lecture Notes in Computer Science 6925, Springer 2011, ISBN 978-3-642-24411-7
Editors' Introduction
- Jyrki Kivinen, Csaba Szepesvári, Esko Ukkonen, Thomas Zeugmann:
Editors' Introduction. 1-13
Invited Papers
- Peter Auer, Shiau Hong Lim, Chris Watkins:
Models for Autonomously Motivated Exploration in Reinforcement Learning - (Extended Abstract). 14-17 - Yoshua Bengio, Olivier Delalleau:
On the Expressive Power of Deep Architectures. 18-36 - Jorma Rissanen:
Optimal Estimation. 37 - Eyke Hüllermeier, Johannes Fürnkranz:
Learning from Label Preferences. 38 - Ming Li:
Information Distance and Its Extensions. 39
Inductive Inference
- Timo Kötzing:
Iterative Learning from Positive Data and Counters. 40-54 - Sanjay Jain, Eric Martin, Frank Stephan:
Robust Learning of Automatic Classes of Languages. 55-69 - Sanjay Jain, Eric Martin, Frank Stephan:
Learning and Classifying. 70-83 - Michael Geilke, Sandra Zilles:
Learning Relational Patterns. 84-98
Regression
- Sébastien Gerchinovitz, Jia Yuan Yu:
Adaptive and Optimal Online Linear Regression on ℓ1-Balls. 99-113 - Nina Vaits, Koby Crammer:
Re-adapting the Regularization of Weights for Non-stationary Regression. 114-128 - Arnak S. Dalalyan, Joseph Salmon:
Competing against the Best Nearest Neighbor Filter in Regression. 129-143
Bandit Problems
- Sébastien Bubeck, Gilles Stoltz, Jia Yuan Yu:
Lipschitz Bandits without the Lipschitz Constant. 144-158 - Antoine Salomon, Jean-Yves Audibert:
Deviations of Stochastic Bandit Regret. 159-173 - Aurélien Garivier, Eric Moulines:
On Upper-Confidence Bound Policies for Switching Bandit Problems. 174-188 - Alexandra Carpentier, Alessandro Lazaric, Mohammad Ghavamzadeh, Rémi Munos, Peter Auer:
Upper-Confidence-Bound Algorithms for Active Learning in Multi-armed Bandits. 189-203
Online Learning
- Constantinos Panagiotakopoulos, Petroula Tsampouka:
The Perceptron with Dynamic Margin. 204-218 - Manfred K. Warmuth, Wouter M. Koolen, David P. Helmbold:
Combining Initial Segments of Lists. 219-233 - Eyal Gofer, Yishay Mansour:
Regret Minimization Algorithms for Pricing Lookback Options. 234-248 - Chi-Jen Lu, Wei-Fu Lu:
Making Online Decisions with Bounded Memory. 249-261 - Tor Lattimore, Marcus Hutter, Vaibhav Gavane:
Universal Prediction of Selected Bits. 262-276 - Brendan Juba, Santosh S. Vempala:
Semantic Communication for Simple Goals Is Equivalent to On-line Learning. 277-291
Kernel and Margin Based Methods
- Xinhua Zhang, Ankan Saha, S. V. N. Vishwanathan:
Accelerated Training of Max-Margin Markov Networks with Kernels. 292-307 - Corinna Cortes, Mehryar Mohri:
Domain Adaptation in Regression. 308-323 - Daiki Suehiro, Kohei Hatano, Eiji Takimoto:
Approximate Reduction from AUC Maximization to 1-Norm Soft Margin Optimization. 324-337
Intelligent Agents
- Peter Sunehag, Marcus Hutter:
Axioms for Rational Reinforcement Learning. 338-352 - Laurent Orseau:
Universal Knowledge-Seeking Agents. 353-367 - Tor Lattimore, Marcus Hutter:
Asymptotically Optimal Agents. 368-382 - Tor Lattimore, Marcus Hutter:
Time Consistent Discounting. 383-397
Other Learning Models
- Anna Kasprzik, Ryo Yoshinaka:
Distributional Learning of Simple Context-Free Tree Grammars. 398-412 - Elena Grigorescu, Lev Reyzin, Santosh S. Vempala:
On Noise-Tolerant Learning of Sparse Parities and Related Problems. 413-424 - Malte Darnstädt, Hans Ulrich Simon, Balázs Szörényi:
Supervised Learning and Co-training. 425-439 - Shalev Ben-David, Shai Ben-David:
Learning a Classifier when the Labeling Is Known. 440-451
Erratum
- Samuel E. Moelius, Sandra Zilles:
Erratum: Learning without Coding. 452
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