By Kamalika Chaudhuri, CLAUDIO GENTILE, Sandra Zilles
This ebook constitutes the court cases of the twenty sixth overseas 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 technology, DS 2015. The 23 complete papers awarded during this quantity have been conscientiously reviewed and chosen from forty four submissions. additionally the e-book includes 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 thought and algorithms; statistical studying concept and pattern complexity; on-line studying, stochastic optimization; and Kolmogorov complexity, algorithmic details theory.
Read or Download Algorithmic Learning Theory: 26th International Conference, ALT 2015, Banff, AB, Canada, October 4-6, 2015, Proceedings (Lecture Notes in Computer Science) PDF
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Extra info for Algorithmic Learning Theory: 26th International Conference, ALT 2015, Banff, AB, Canada, October 4-6, 2015, Proceedings (Lecture Notes in Computer Science)
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. : Guaranteed minimum rank approximation from linear observations by nuclear norm minimization with an ellipsoidal constraint. 4742 (2009) 18 K. Zhong et al. 17. : Universal low-rank matrix recovery from pauli measurements.
Again, we see similar distinctions to the matrix case. In the matrix case, the only local maximizers of the Rayleigh quotient are the eigenvectors with the largest eigenvalue (and these maximizers take on the globally optimal value). For the case of orthogonal tensor forms, the robust eigenvectors are precisely the isolated local maximizers. , the tensor analogue of the matrix power method). Moreover, a second-derivative test based on T (I, I, u) can be employed to test for local optimality and rule out other stationary points.
Matrix Computations. Johns Hopkins University Press (1996) 16. : Learning mixtures of spherical Gaussians: moment methods and spectral decompositions. In: Fourth Innovations in Theoretical Computer Science (2013) 17. : Identiﬁability and unmixing of latent parse trees. In: Advances in Neural Information Processing Systems 25 (2012) 18. : A spectral algorithm for learning hidden Markov models. Journal of Computer and System Sciences 78(5), 1460–1480 (2012) 19. : Independent component analysis: algorithms and applications.