Download Algorithmic Learning Theory: 25th International Conference, by Peter Auer, Alexander Clark, Thomas Zeugmann, Sandra Zilles PDF

By Peter Auer, Alexander Clark, Thomas Zeugmann, Sandra Zilles

This e-book constitutes the lawsuits of the twenty fifth overseas convention on Algorithmic studying concept, ALT 2014, held in Bled, Slovenia, in October 2014, and co-located with the seventeenth overseas convention on Discovery technology, DS 2014. The 21 papers awarded during this quantity have been rigorously reviewed and chosen from 50 submissions. furthermore the booklet includes four complete papers summarizing the invited talks. The papers are geared up in topical sections named: inductive inference; special studying from queries; reinforcement studying; on-line studying and studying with bandit info; statistical studying concept; privateness, clustering, MDL, and Kolmogorov complexity.

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Additional resources for Algorithmic Learning Theory: 25th International Conference, ALT 2014, Bled, Slovenia, October 8-10, 2014. Proceedings

Sample text

1≤i 1/2 for any pair of items ai and aj . 4 Moreover, as shown by Mallows [36], the pairwise probabilities can be calculated analytically as functions of the model parameters φ and r as follows: Assume the Mallows model with parameters φ and r.

Concretely, the Copeland and the sum of expectations (or Borda counts) procedure are used in their study. 1. The sample complexity of the implementations in [43] are of order K 2 in general. Just like in [11], this is the price to pay for a “model-free” learning procedure that does not make any assumptions on the structure of the preference matrix. The analysis of the authors is more general, because they also investigate the infinite horizon case, where a time limit is not given in advance. 5 Summary and Perspectives This paper provides a survey of the state-of-the-art in preference-based online learning with bandit algorithms, an emerging research field that we referred to as preference-based multi-armed bandits (PB-MAB).

More specifically, with P(r) the probability of observing the ranking r, the probability qi,j that ai is preferred to aj is obtained by summing over all rankings r in which ai precedes aj : qi,j = P(ai aj ) = P(r) (4) r∈L(rj >ri ) where L(rj > ri ) = {r ∈ SK | rj > ri } denotes the subset of permutations for which the rank rj of aj is higher than the rank ri of ai (smaller ranks indicate higher preference). In this setting, the learning problem essentially comes down to making inference about P based on samples in the form of pairwise comparisons.

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