The Special Issue on

Algorithmic Learning Theory

for ALT 2011 in

Theoretical Computer Science

ALT 11 Logo

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.

Table of Contents

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 ,

pp. 4–28

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

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

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

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

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

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

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

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

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