By Kamalika Chaudhuri, CLAUDIO GENTILE, Sandra Zilles
This booklet constitutes the complaints of the twenty sixth overseas convention on Algorithmic studying concept, ALT 2015, held in Banff, AB, Canada, in October 2015, and co-located with the 18th overseas convention on Discovery technology, DS 2015. The 23 complete papers provided during this quantity have been conscientiously reviewed and chosen from forty four submissions. furthermore the publication 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, educating complexity; computational studying thought and algorithms; statistical studying thought and pattern complexity; on-line studying, stochastic optimization; and Kolmogorov complexity, algorithmic info theory.
Read Online or Download Algorithmic Learning Theory: 26th International Conference, ALT 2015, Banff, AB, Canada, October 4-6, 2015, Proceedings PDF
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Extra resources for Algorithmic Learning Theory: 26th International Conference, ALT 2015, Banff, AB, Canada, October 4-6, 2015, Proceedings
Journal of Symbolic Computation 40(6), 1302–1324 (2005) 31. : Subtracting a best rank-1 approximation may increase tensor rank. Linear Algebra and Its Applications 433, 1276–1300 (2010) 32. : Perturbation bounds in connection with singular value decomposition. BIT Numerical Mathematics 12(1), 99–111 (1972) 33. : Rank-one approximation to high order tensors. sg Abstract. In iterative learning the memory of the learner can only be updated when the hypothesis changes; this results in only ﬁnitely many updates of memory during the overall learning history.
6 Experiments In this section, we demonstrate empirically that our Gaussian rank-one linear operators are signiﬁcantly more eﬃcient for matrix sensing than the existing RIP based measurement operators. In particular, we apply the two recovery methods namely alternating minimization (ALS) and nuclear norm minimization (Nuclear) to the measurements obtained using three diﬀerent operators: rankone independent (Rank1 Indep), rank-one dependent (Rank1 Dep), and a RIP based operator generated using random Gaussian matrices (RIP).
The problem can then be solved by a variety of approaches, including ﬁxed-point and variational methods. One approach for obtaining the orthogonal decomposition is the tensor power method of [21, Remark3]. We provide a convergence analysis of this method for orthogonally decomposable symmetric tensors, as well as a robust (and computationally tractable) variant. The perturbation analysis in  can be viewed as an analogue of Wedin’s perturbation theorem for singular vectors of matrices , providing a bound on the error of the recovered decomposition in terms of the operator norm of the tensor perturbation.