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stat.ML2020★ 7 cited
Feature space approximation for kernel-based supervised learning
Patrick Gelß, Stefan Klus, Ingmar Schuster +1
We propose a method for the approximation of high- or even infinite-dimensional feature vectors, which play an important role in supervised learning. The goal is to reduce the size…
stat.ML2018
Analyzing high-dimensional time-series data using kernel transfer operator eigenfunctions
Stefan Klus, Sebastian Peitz, Ingmar Schuster
Kernel transfer operators, which can be regarded as approximations of transfer operators such as the Perron-Frobenius or Koopman operator in reproducing kernel Hilbert spaces, are…
stat.ML2018
Markov Chain Importance Sampling -- a highly efficient estimator for MCMC
Ingmar Schuster, Ilja Klebanov
Markov chain (MC) algorithms are ubiquitous in machine learning and statistics and many other disciplines. Typically, these algorithms can be formulated as acceptance rejection met…