[Statlist] Next talk: Friday, 03.11.2017, with Zoltan Szabo, Université Paris-Sarclay

[Maurer Letizia]

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ETH and University of Zurich

Organisers:

Proff. P. Bühlmann - L. Held - T. Hothorn - M. Maathuis -
N. Meinshausen - S. van de Geer - M. Wolf

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We are glad to announce the following talk:

Friday, 03.11.2017, 2017 at 15.15h  ETH Zurich HG G19.1
with Zoltan Szabo, Université Paris-Sarclay

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Title:

Tensor Product Kernels: Characteristic Property and Universality<https://www.math.ethz.ch/sfs/news-and-events/research-seminar.html?s=hs17#e_10640>

Abstract:

Maximum mean discrepancy (MMD) and Hilbert-Schmidt independence criterion (HSIC) are among the most popular and successful approaches in applied mathematics to measure the difference and the independence of random variables, respectively. Thanks to their kernel-based foundations, MMD and HSIC are applicable on a large variety of domains such as documents, images, trees, graphs, time series, dynamical systems, sets or permutations. Despite their tremendous practical success, quite little is known about when HSIC characterizes independence and MMD with tensor kernel can discriminate probability distributions, in terms of the contributing kernel components. In this talk, I am going to provide a complete answer to this question, with conditions which are often easy to verify in practice. [Joint work with Bharath K. Sriperumbudur (PSU). Preprint: https://arxiv.org/abs/1708.08157, ITE toolbox (estimators): https://bitbucket.org/szzoli/ite-in-python/]

This abstract is also to be found under the following link: http://stat.ethz.ch/events/research_seminar

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