Algorithmic Learning Theory: 24th International Conference, by Sanjay Jain, Rémi Munos, Frank Stephan, Thomas Zeugmann

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

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

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Preference learning in recommender systems. : A bound on the label complexity of agnostic active learning. In: Proceedings of the 24th International Conference on Machine Learning, ICML 2007, pp. : Label ranking by learning pairwise preferences. Artif. Intell. : Statistical ranking and combinatorial hodge theory. Math. Program. : Optimizing search engines using clickthrough data. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. : Mathematics without numbers.

ALT 2013. LNCS (LNAI), vol. 8139, pp. 56–70. : New 3/4-approximation algorithms for the maximum satisfiability problem. : The convex optimization approach to regret minimization. J. ) Optimization for Machine Learning, ch. 10, pp. 287–304. : Projection-free online learning. : Learning Permutations with Exponential Weights. : Submodular function minimization. Mathematical Programming, Ser. : Playing games with approximation algorithms. : Efficient algorithms for online decision problems. : Hedging Structured Concepts.

Feed t to B and resume it. Now we state the main theorems. Theorem 8. Under Assumption 2, Algorithm 3 runs in polynomial time per trial and achieves α-regret to be at most αReg B (T ). Theorem 9. Under Assumption 1, there exists an algorithm that runs in poly(n, 1/ ) time and achieves (α + )-regret to be at most (α + )Reg B (T ), where > 0 is a parameter that can be arbitrarily chosen. The last theorem is proved by explicitly constructing a metarounding algorithm by using the α-approximation Algorithm A of Assumption 1.

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