Special Issue on Algorithmic Learning Theory for ALT 2011

The Special Issue on

for ALT 2011 in

Theoretical Computer Science

appeared in

Theoretical Computer Science, Volume 519, January 30, 2014.

The Special Issue on Algorithmic Learning Theory has been edited by Jyrki Kivinen, Csaba Szepesvári, and Thomas Zeugmann.

Jyrki Kivinen, Csaba Szepesvári, and Thomas Zeugmann Guest Editors' Introduction,	pp. 1–3

Sébastien Gerchinovitz, and Jia Yuan Yu Adaptive and Optimal Online Linear Regression on ℓ¹-Balls , Abstract.	pp. 4–28

Manfred K. Warmuth, Wouter M. Koolen, and David P. Helmbold. Combining Initial Segments of Lists , Abstract.	pp. 29–45

Antoine Salomon and Jean-Yves Audibert. Robustness of Stochastic Bandit Policies , Abstract.	pp. 46–67

Malte Darnstädt, Hans Ulrich Simon, and Balázs Szörényi. Supervised Learning and Co-training, Abstract.	pp. 68–87

Xinhua Zhang, Ankan Saha, and S. V. N. Vishwanathan. Accelerated Training of Max-margin Markov Networks with Kernels, Abstract.	pp. 88–102

Corinna Cortes and Mehryar Mohri. Domain Adaptation and Sample Bias Correction Theory and Algorithm for Regression , Abstract.	pp. 103–126

Laurent Orseau. Universal Knowledge-seeking Agents , Abstract.	pp. 127–139

Tor Lattimore and Marcus Hutter General Time Consistent Discounting , Abstract.	pp. 140–154

Timo Kötzing. Iterative Learning from Positive Data and Counters, Abstract.	pp. 155–169
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