N. Baldin (University of Cambridge, GB)
Wednesday, April 25, 2018 - 10:00
Mohrenstr. 39, 10117 Berlin, Erhard-Schmidt-Hörsaal, Erdgeschoss
Forschungsseminar Mathematische Statistik
In this talk, we will consider the problem of link prediction, based on partial observation of a large network, and on side information associated to its vertices. The generative model is formulated as a matrix logistic regression. The performance of the model is analysed in a high-dimensional regime under a structural assumption. The minimax rate for the Frobenius-norm risk is established and a combinatorial estimator based on the penalised maximum likelihood approach is shown to achieve it. Furthermore, it is shown that this rate cannot be attained by any (randomised) algorithm computable in polynomial time under a computational complexity assumption. (joint work with Q. Berthet)
submitted by chschnei (christine.schneider@wias-berlin.de, 030 20372574)