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

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

ISBN-10: 3642409342

ISBN-13: 9783642409349

ISBN-10: 3642409350

ISBN-13: 9783642409356

This e-book constitutes the complaints of the twenty fourth overseas convention on Algorithmic studying thought, ALT 2013, held in Singapore in October 2013, and co-located with the sixteenth overseas convention on Discovery technological know-how, DS 2013. The 23 papers offered during this quantity have been rigorously reviewed and chosen from 39 submissions. additionally the ebook comprises 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 idea, statistical studying concept, 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

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Moreover, in this case, it is known that C does not have efficient low-regret algorithms. Examples of such classes C are permutations (with Kendall tau loss) [27], set covers, and truth assignments to a CNF formula, for which the corresponding offline linear optimization problems are the minimum feedback arc set problem, the minimum set cover problem, and the MAXSAT problem, respectively. So we change our goal. Assume that we have an α-approximation algorithm for OPT(C). Then our second goal is to minimize the following α-regret: T T ct · E[ t=1 c∗ · t ] − α min ∗ c ∈C t.

2. 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.

New Direction 2. With the above model one can also define learning of sequence of functions or functions that change with time. For example, suppose the teacher can change the target function after each query from f to one of the functions in the set M (f ) and the learner knows M . Then ANSWER(i, d, f ) can be defined as fi (d) for some sequence of fi ∈ M (fi−1 ) where f1 = f . 4 Learning Algorithm and Complexity The learning algorithm can be sequential or parallel, deterministic or randomized and adaptive or non-adaptive.

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Algorithmic Learning Theory: 24th International Conference, ALT 2013, Singapore, October 6-9, 2013. Proceedings by Sanjay Jain, Rémi Munos, Frank Stephan, Thomas Zeugmann


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