By Sanjay Jain, Rémi Munos, Frank Stephan, Thomas Zeugmann
This publication constitutes the complaints of the twenty fourth foreign convention on Algorithmic studying idea, 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. moreover the publication includes three complete papers of invited talks. The papers are prepared in topical sections named: on-line studying, inductive inference and grammatical inference, instructing and studying from queries, bandit thought, statistical studying thought, Bayesian/stochastic studying, and unsupervised/semi-supervised learning.
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Additional resources for Algorithmic Learning Theory: 24th International Conference, ALT 2013, Singapore, October 6-9, 2013. Proceedings
M´emoire sur les ´elections au scrutin. : Noisy sorting without resampling. In: SODA 2008: Proceedings of the Nineteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. : Here or there: Preference judgments for relevance. W. ) ECIR 2008. LNCS, vol. 4956, pp. 16–27. : Learning to order things. J. Artif. Intell. Res. : Essai sur l’application de l’analyse a ` la probabilit´e des d´ecisions rendues a ` la pluralit´e des voix. : Ordering by weighted number of wins gives a good ranking for weighted tournaments.
We have mentioned the need for active learning techniques when learning from choices and preferences in settings in which the system can choose which pairs or alternative sets to learn from. In certain cases, however, the system cannot simply prompt individuals and ask them about their preferences. It can only passively collect their choice behaviour patterns. Such, for example, is the case of learning from clickthrough data as described above. In some cases, however, it is possible for the system to slightly manipulate search or recommendation results presented to the user so as to gain more information.
There is a polynomial time algorithm that ﬁnds c ∈ C such that c · α minx∈P x · for a given loss vector ∈ Rn+ . ≤ 24 E. Takimoto and K. Hatano √ 3. There is an online algorithm for the concept class P that achives O( T ) regret and runs in time polynomial in n per trial. This assumption is motivated by the fact that many combinatorial optimization problems have LP or SDP relaxation schemes. All the classes mentioned above satisfy the assumption and thus we have eﬃcient online algorithms for these classes.