Overview and Recent Advances in Partial Least Squares. - Roman Rosipal; Nicole Krämer 002 (SLSFS, 2005)

Kernelizing PLS, degrees of freedom, and efficient model selection. - Nicole Krämer 002; Mikio L. Braun (ICML, 2007)

An overview on the shrinkage properties of partial least squares regression. - Nicole Krämer 002 (Comput. Stat., 2007)

Partial least squares regression for graph mining. - Hiroto Saigo; Nicole Krämer 002; Koji Tsuda (KDD, 2008)

The Feature Importance Ranking Measure. - Alexander Zien; Nicole Krämer 002; Sören Sonnenburg; Gunnar Rätsch (ECML/PKDD (2), 2009)

Regularized estimation of large-scale gene association networks using graphical Gaussian models. - Nicole Krämer 002; Juliane Schäfer; Anne-Laure Boulesteix (BMC Bioinform., 2009)

Lanczos Approximations for the Speedup of Kernel Partial Least Squares Regression. - Nicole Krämer 002; Masashi Sugiyama; Mikio L. Braun (AISTATS, 2009)

Time Domain Parameters as a feature for EEG-based Brain-Computer Interfaces. - Carmen Vidaurre; Nicole Krämer 002; Benjamin Blankertz; Alois Schlögl (Neural Networks, 2009)

Optimal learning rates for Kernel Conjugate Gradient regression. - Gilles Blanchard; Nicole Krämer 002 (NIPS, 2010)

ASAP: Automatic Semantics-Aware Analysis of Network Payloads. - Tammo Krueger; Nicole Krämer 002; Konrad Rieck (PSDML, 2010)

Kernel Partial Least Squares is Universally Consistent. - Gilles Blanchard; Nicole Krämer 002 (AISTATS, 2010)

Sparse Causal Discovery in Multivariate Time Series. - Stefan Haufe; Klaus-Robert Müller; Guido Nolte; Nicole Krämer 002 (NIPS Causality: Objectives and Assessment, 2010)

Comparison of Granger Causality and Phase Slope Index. - Guido Nolte; Andreas Ziehe; Nicole Krämer 002; Florin Popescu; Klaus-Robert Müller (NIPS Causality: Objectives and Assessment, 2010)

Learning stateful models for network honeypots. - Tammo Krueger; Hugo Gascon; Nicole Krämer 002; Konrad Rieck (AISec, 2012)