By Kamalika Chaudhuri, CLAUDIO GENTILE, Sandra Zilles
This e-book 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 overseas convention on Discovery technology, DS 2015. The 23 complete papers offered during this quantity have been rigorously reviewed and chosen from forty four submissions. furthermore the booklet 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 idea and algorithms; statistical studying thought 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 PDF
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Extra info for Algorithmic Learning Theory: 26th International Conference, ALT 2015, Banff, AB, Canada, October 4-6, 2015, Proceedings
Zhong et al. s. s. 6 2000 1500 1000 500 0 50 100 d Indep. 2 500 00 50 100 d Dep. ALS 150 Number of Measurements 3000 1 2500 Number of Measurements 3000 Number of Measurements Number of Measurements Fig. 1. 2 500 00 50 100 d Indep. 2 00 50 100 d 150 0 Dep. Nuclear Fig. 2. Recovery rate for diﬀerent matrix dimension d (x-axis) and diﬀerent number of measurements m (y-axis). The color reﬂects the recovery rate scaled from 0 to 1. The white color indicates perfect recovery, while the black color denotes failure in all the experiments.
K ) ∈ Rk×k is diagonal matrix of non-zero eigenvalues. In other words, k k λ i vi vi = M= i=1 λi vi⊗2 . (1) i=1 Such a decomposition is guaranteed to exist for every symmetric matrix. Recovery of the vi ’s and λi ’s can be viewed at least two ways. First, each vi is ﬁxed under the mapping u → M u, up to a scaling factor λi : k M vi = λj (vj vi )vj = λi vi j=1 as vj vi = 0 for all j = i by orthogonality. The vi ’s are not necessarily the only such ﬁxed points. For instance, with the multiplicity λ1 = λ2 = λ, then any linear combination of v1 and v2 is similarly ﬁxed under M .
IEEE (2014) 8. : Fast matrix completion without the condition number. In: Proceedings of The 27th Conference on Learning Theory, pp. 638–678 (2014) 9. : Quic & dirty: A quadratic approximation approach for dirty statistical models. In: Advances in Neural Information Processing Systems, pp. 2006–2014 (2014) 10. : Nuclear norm minimization via active subspace selection. In: Proceedings of The 31st International Conference on Machine Learning, pp. 575–583 (2014) 11. : Provable inductive matrix completion (2013).