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

By Kamalika Chaudhuri, CLAUDIO GENTILE, Sandra Zilles

This booklet constitutes the lawsuits 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 awarded during this quantity have been conscientiously reviewed and chosen from forty four submissions. additionally the booklet comprises 2 complete papers summarizing the invited talks and a couple of abstracts of invited talks. The papers are geared up in topical sections named: inductive inference; studying from queries, instructing complexity; computational studying concept and algorithms; statistical studying conception and pattern complexity; on-line studying, stochastic optimization; and Kolmogorov complexity, algorithmic details theory.

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Read or Download Algorithmic Learning Theory: 26th International Conference, ALT 2015, Banff, AB, Canada, October 4-6, 2015, Proceedings PDF

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In: Proceedings of The 31st International Conference on Machine Learning, pp. 575–583 (2014) 11. : Provable inductive matrix completion (2013). CoRR. 0626 12. : Guaranteed rank minimization via singular value projection. In: NIPS, pp. 937–945 (2010) 13. : Low-rank matrix completion using alternating minimization. In: STOC (2013) 14. : Matrix completion from a few entries. IEEE Transactions on Information Theory 56(6), 2980–2998 (2010) 15. : Low rank matrix recovery from rank one measurements. 6913 (2014) 16.

Fixed-Point Characterization. For a tensor T , consider the vector-valued map u → T (I, u, u) (5) Tensor Decompositions for Learning Latent Variable Models 31 which is the third-order generalization of (2). This can be explicitly written as d Ti,j,l (ej u)(el u)ei . T (I, u, u) = i=1 1≤j,l≤d Observe that (5) is not a linear map, which is a key difference compared to the matrix case. An eigenvector u for a matrix M satisfies M (I, u) = λu, for some scalar λ. We say a unit vector u ∈ Rn is an eigenvector of T , with corresponding eigenvalue λ ∈ R, if T (I, u, u) = λu.

X are exchangeable if their joint probability distribution is invariant to permutation of the indices. The well-known De Finetti’s theorem [5] implies that such exchangeable models can be viewed as mixture models in which there is a latent variable h such that x1 , x2 , . . d. given h (see Figure 1(a) for the corresponding graphical model) and the conditional distributions are identical at all the nodes. In our simplified topic model for documents, the latent variable h is interpreted as the (sole) topic of a given document, and it is assumed to take only a finite number of distinct values.

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