By Peter Auer, Alexander Clark, Thomas Zeugmann, Sandra Zilles
This ebook constitutes the complaints of the twenty fifth foreign 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 provided during this quantity have been conscientiously reviewed and chosen from 50 submissions. furthermore the booklet comprises four complete papers summarizing the invited talks. The papers are prepared in topical sections named: inductive inference; designated studying from queries; reinforcement studying; on-line studying and studying with bandit info; statistical studying conception; privateness, clustering, MDL, and Kolmogorov complexity.
Read Online or Download Algorithmic Learning Theory: 25th International Conference, ALT 2014, Bled, Slovenia, October 8-10, 2014. Proceedings PDF
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Additional resources for Algorithmic Learning Theory: 25th International Conference, ALT 2014, Bled, Slovenia, October 8-10, 2014. Proceedings
Therefore, it cannot be directly linked to the expected sample complexity. In order to deﬁne the expected sample complexity, the learning algorithm needs to terminate in a ﬁnite number of steps with probability 1. Under this condition, running a learning algorithm on the same bandit instance results in a ﬁnite sample complexity, which is a random number distributed according to an unknown law P : N → [0, 1]. The distribution P has ﬁnite support, since the algorithm terminates in a ﬁnite number of steps in every case.
H¨ ullermeier Cumulative Regret In a preference-based setting, deﬁning a reasonable regret is not as straightforward as in the value-based setting, where the sub-optimality of an action can be expressed easily on a numerical scale. In particular, since the learner selects two arms to be compared in an iteration, the sub-optimality of both of these arms should be taken into account. A commonly used deﬁnition of regret is the following [46, 43, 47, 45]: Suppose the learner selects arms ai(t) and aj(t) in time step t.
Note that the inequality is trivially valid if A does not terminate before T . The same argument as given above for the case of expected regret also holds for high probability regret bounds in the explore-then-exploit framework. In summary, the performance of an explore-then-exploit algorithm is bounded by the performance of the exploration algorithm. More importantly, since the per round regret is at most one, the sample complexity of the exploration algorithm readily upper-bounds the expected regret; this fact was pointed out in [46, 44].
Algorithmic Learning Theory: 25th International Conference, ALT 2014, Bled, Slovenia, October 8-10, 2014. Proceedings by Peter Auer, Alexander Clark, Thomas Zeugmann, Sandra Zilles