Special Issue on Algorithmic Learning Theory for ALT 2015

The Special Issue on

Algorithmic Learning Theory

for ALT 2015 in

Theoretical Computer Science

appeared in

Theoretical Computer Science Volume 716, March 15, 2018.

The Special Issue on Algorithmic Learning Theory has been edited by Kamalika Chaudhuri, and Claudio Gentile.

Table of Contents

Kamalika Chaudhuri, and Claudio Gentile Guest Editors' Introduction	pp. 1–3

Montserrat Hermo and Ana Ozaki. Exact learning of multivalued dependency formulas , Abstract.	pp. 4–14

Hasan Abasi, Nader H. Bshouty, and Hanna Mazzawi. Non-adaptive learning of a hidden hypergraph, Abstract.	pp. 15–27

Jan Leike and Marcus Hutter. . On the computability of Solomonoff induction and AIXI, Abstract.	pp. 28–49

Francesco Orabona and Dávid Pál. Scale-free online learning, Abstract.	pp. 50–69

Malte Darnstädt, Christoph Ries, and Hans Ulrich Simon. Hierarchical design of fast Minimum Disagreement algorithms, Abstract.	pp. 70–88

Borja Balle and Mehryar Mohri. Generalization bounds for learning weighted automata, Abstract.	pp. 89–106

Ziyuan Gao, Hans Ulrich Simon, and Sandra Zilles. On the teaching complexity of linear sets, Abstract.	pp. 107–123

Liu Yang, Steve Hanneke, and Jaime Carbonell. Bounds on the minimax rate for estimating a prior over a VC class from independent learning tasks. Abstract.	pp. 124–140
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