The 22nd International Conference
Algorithmic Learning Theory

Aalto University, Espoo, Finland
October 5 - 7, 2011


     The following papers have been accepted for ALT 2011.
     There is no particular order in the list.

     Please keep in mind that we need your final version until

July 15, 2011.
     Please prepare your final version in accordance with the 
     Instructions for authors.

Axioms for Rational Reinforcement Learning Peter Sunehag and Marcus Hutter

Robust Learning of Automatic Classes of Languages Sanjay Jain, Eric Martin, and Frank Stephan

Learning and Classifying Sanjay Jain, Eric Martin, and Frank Stephan

Making Online Decisions with Bounded Memory Chi-Jen Lu and Wei-Fu Lu

Lipschitz Bandits without the Lipschitz Constant Sebastien Bubeck, Gilles Stoltz, and Jia Yuan Yu

Learning Relational Patterns Michael Geilke and Sandra Zilles

Distributional Learning of Simple Context-Free Tree Grammars Ryo Yoshinaka and Anna Kasprzik

Accelerated Training of Max-Margin Markov Networks with Kernels Xinhua Zhang, Ankan Saha, and S.V.N. Vishwanathan

On Upper-Confidence Bound Policies for Switching Bandit Problems Aurélien Garivier and Eric Moulines

Universal Knowledge-Seeking Agents Laurent Orseau

Asymptotically Optimal Agents Tor Lattimore and Marcus Hutter

Iterative Learning from Positive Data and Counters Timo Kötzing

Time Consistent Discounting Tor Lattimore and Marcus Hutter

Adaptive and Optimal Online Linear Regression on L1-balls Sebastien Gerchinovitz and Jia Yuan Yu

Deviations of Stochastic Bandit Regret Antoine Salomon and Jean-Yves Audibert

The Perceptron with Dynamic Margin Constantinos Panagiotakopoulos and Petroula Tsampouka

Domain Adaptation in Regression Corinna Cortes and Mehryar Mohri

Universal Prediction of Selected Bits Tor Lattimore, Marcus Hutter, and Vaibhav Gavane

Regret Minimization Algorithms for Pricing Lookback Options Eyal Gofer and Yishay Mansour

Upper-Confidence-Bound Algorithms for Active Learning in Multi-Armed Bandits Alexandra Carpentier, Alessandro Lazaric, Mohammad Ghavamzadeh, Rémi Munos, and Peter Auer

On Noise-Tolerant Learning of Sparse Parities and Related Problems Elena Grigorescu, Lev Reyzin, and Santosh Vempala

Supervised Learning and Co-training Hans Simon, Balázs Szörényi, and Malte Darnstädt

Re-Adapting the Regularization of Weights for Non-Stationary Regression Nina Vaits and Koby Crammer

Semantic Communication for Simple Goals is Equivalent to On-Line Learning Brendan Juba and Santosh Vempala

Learning a Classifier When the Labeling is Known Shalev Ben-David and Shai Ben-David

Approximate Reduction from AUC Maximization to 1-norm Soft Margin Optimization Daiki Suehiro, Kohei Hatano and Eiji Takimoto

Competing Against the Best Nearest Neighbor Filter in Regression Arnak S. Dalalyan and Joseph Salmon

Combining Initial Segments of Lists Manfred Warmuth, Wouter M. Koolen, and David P. Helmbold

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