Algorithmic Learning Theory: 26th International Conference, by Kamalika Chaudhuri, CLAUDIO GENTILE, Sandra Zilles PDF

By Kamalika Chaudhuri, CLAUDIO GENTILE, Sandra Zilles

ISBN-10: 331924485X

ISBN-13: 9783319244853

ISBN-10: 3319244868

ISBN-13: 9783319244860

This booklet constitutes the court cases of the twenty sixth foreign convention on Algorithmic studying conception, ALT 2015, held in Banff, AB, Canada, in October 2015, and co-located with the 18th foreign convention on Discovery technological know-how, DS 2015. The 23 complete papers offered during this quantity have been rigorously reviewed and chosen from forty four submissions. moreover the ebook comprises 2 complete papers summarizing the invited talks and a pair of abstracts of invited talks. The papers are prepared in topical sections named: inductive inference; studying from queries, educating complexity; computational studying idea and algorithms; statistical studying idea and pattern complexity; on-line studying, stochastic optimization; and Kolmogorov complexity, algorithmic info theory.

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Extra info for Algorithmic Learning Theory: 26th International Conference, ALT 2015, Banff, AB, Canada, October 4-6, 2015, Proceedings

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C Springer International Publishing Switzerland 2015 K. Chaudhuri et al. ): ALT 2015, LNAI 9355, pp. 41–55, 2015. 1007/978-3-319-24486-0 3 42 S. Jain et al. corresponding datum was observed or not. In its most restrictive form, this type of learning is called incremental or iterative learning [3,8,13]. An iterative learner can memorise data only when it revises the hypothesis and not at any other point of time; thus it can, through the overall learning history, only finitely often revise its long-term memory.

Tensor decompositions for learning latent variable models. Journal of Machine Learning Research 15, (2014) Tensor Decompositions for Learning Latent Variable Models 37 2. : A spectral algorithm for latent Dirichlet allocation. In: Advances in Neural Information Processing Systems 25, (2012) 3. : Learning mixtures of tree graphical models. In: Advances in Neural Information Processing Systems 25 (2012) 4. : A method of moments for mixture models and hidden Markov models. In: Twenty-Fifth Annual Conference on Learning Theory, vol.

For t = 1, 2, . . , let T (I, θt−1 , θt−1 ) . T (I, θt−1 , θt−1 ) θt := Then k v1 − θt 2 ≤ λ−2 i 2λ21 i=2 λ 2 v2 θ 0 · λ 1 v1 θ 0 2t+1 . That is, repeated iteration of (6) starting from θ0 converges to v1 at a quadratic rate. To obtain all eigenvectors, we may simply proceed iteratively using deflation, executing the power method on T − j λj vj⊗3 after having obtained robust eigenvector / eigenvalue pairs {(vj , λj )}. Proof. Let θ0 , θ1 , θ2 , . . be the sequence given by θ0 := θ0 and θt := T (I, θt−1 , θt−1 ) for t ≥ 1.

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Algorithmic Learning Theory: 26th International Conference, ALT 2015, Banff, AB, Canada, October 4-6, 2015, Proceedings by Kamalika Chaudhuri, CLAUDIO GENTILE, Sandra Zilles


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