By Sanjay Jain, Rémi Munos, Frank Stephan, Thomas Zeugmann

This ebook constitutes the court cases of the twenty fourth foreign convention on Algorithmic studying thought, ALT 2013, held in Singapore in October 2013, and co-located with the sixteenth foreign convention on Discovery technological know-how, DS 2013. The 23 papers provided during this quantity have been rigorously reviewed and chosen from 39 submissions. furthermore the ebook comprises three complete papers of invited talks. The papers are geared up in topical sections named: on-line studying, inductive inference and grammatical inference, educating and studying from queries, bandit concept, statistical studying concept, Bayesian/stochastic studying, and unsupervised/semi-supervised learning.

**Read Online or Download Algorithmic Learning Theory: 24th International Conference, ALT 2013, Singapore, October 6-9, 2013. Proceedings PDF**

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**Extra info for Algorithmic Learning Theory: 24th International Conference, ALT 2013, Singapore, October 6-9, 2013. Proceedings**

**Example text**

This hardness result result seems to imply part of the aforementioned NP-Hardness result of [13], which was probably achieved independently. Learning and Optimizing with Preferences 19 Very recently, Ailon et al. [1] have studied the problem of designing query eﬃcient active learning algorithms for learning to rank from pairwise preferences. The input to the problem is a noisy preference matrix M (we only assume that M (i, j) ∈ [−1, 1] and that the matrix is skew-symmetric), and the goal is to output a ranking π such that Mπ is as close as possible to M in the 1 norm.

Theorem 6 ( [22]). There exists an algorithm that solves the projection onto B(f ) with respect to the Euclidean distance or the unnormalized relative entropy Eﬃcient Algorithms for Combinatorial Online Prediction 29 in time O(n2 ). Moreover, there exists an algorithm that solves the decomposition for a concept class C in time O(n2 ) if C is the set of extreme points of B(f ) for some cardinality-based submodular function f . 4 Using Oﬄine Approximation Algorithms In this section, we give a short review for the result of [9] which appears in this proceedings.

Nevertheless, we still consider them to be useful sources of information. Whenever a person makes a choice of one alternative from a small set of size two or more, this discrete information encodes a preference. Modeling and analyzing such data has generated much research in various disciplines in the last century. Economists have developed discrete choice models to model and explain, using probabilistic techniques, the choice process of individuals in a market environment in which, over time, they must select one alternative from sets of size at least two.