no code implementations • 25 May 2021 • Danny Panknin, Klaus Robert Müller, Shinichi Nakajima
Assuming that a small number of initial samples are available, we derive the optimal training density that minimizes the generalization error of local polynomial smoothing (LPS) with its kernel bandwidth tuned locally: We adopt the mean integrated squared error (MISE) as a generalization criterion, and use the asymptotic behavior of the MISE as well as the locally optimal bandwidths (LOB) - the bandwidth function that minimizes MISE in the asymptotic limit.
no code implementations • 27 Feb 2019 • Danny Panknin, Stefan Chmiela, Klaus-Robert Müller, Shinichi Nakajima
Inhomogeneities in real-world data, e. g., due to changes in the observation noise level or variations in the structural complexity of the source function, pose a unique set of challenges for statistical inference.
no code implementations • 11 Sep 2016 • Wikor Pronobis, Danny Panknin, Johannes Kirschnick, Vignesh Srinivasan, Wojciech Samek, Volker Markl, Manohar Kaul, Klaus-Robert Mueller, Shinichi Nakajima
In this paper, we propose {multiple purpose LSH (mp-LSH) which shares the hash codes for different dissimilarities.
no code implementations • 25 Sep 2015 • Irene Winkler, Danny Panknin, Daniel Bartz, Klaus-Robert Müller, Stefan Haufe
Inferring causal interactions from observed data is a challenging problem, especially in the presence of measurement noise.
1 code implementation • 11 Jun 2012 • Tammo Krueger, Danny Panknin, Mikio Braun
With the increasing size of today's data sets, finding the right parameter configuration in model selection via cross-validation can be an extremely time-consuming task.