
Symmetric and antisymmetric kernels for machine learning problems in quantum physics and chemistry
We derive symmetric and antisymmetric kernels by symmetrizing and antisy...
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Datadriven model reduction of agentbased systems using the Koopman generator
The dynamical behavior of social systems can be described by agentbased...
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Feature space approximation for kernelbased supervised learning
We propose a method for the approximation of high or even infinitedime...
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GraphKKE: Graph Kernel Koopman Embedding for Human Microbiome Analysis
More and more diseases have been found to be strongly correlated with di...
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Kernelbased approximation of the Koopman generator and Schrödinger operator
Many dimensionality and model reduction techniques rely on estimating do...
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Kernel autocovariance operators of stationary processes: Estimation and convergence
We consider autocovariance operators of a stationary stochastic process ...
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Tensorbased algorithms for image classification
The interest in machine learning with tensor networks has been growing r...
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Datadriven approximation of the Koopman generator: Model reduction, system identification, and control
We derive a datadriven method for the approximation of the Koopman gene...
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Tensorbased EDMD for the Koopman analysis of highdimensional systems
Recent years have seen rapid advances in the datadriven analysis of dyn...
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Kernel Conditional Density Operators
We introduce a conditional density estimation model termed the condition...
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A kernelbased method for coarse graining complex dynamical systems
We present a novel kernelbased machine learning algorithm for identifyi...
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Kernel canonical correlation analysis approximates operators for the detection of coherent structures in dynamical data
We illustrate relationships between classical kernelbased dimensionalit...
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A kernelbased approach to molecular conformation analysis
We present a novel machine learning approach to understanding conformati...
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Singular Value Decomposition of Operators on Reproducing Kernel Hilbert Spaces
Reproducing kernel Hilbert spaces (RKHSs) play an important role in many...
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Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces
Transfer operators such as the PerronFrobenius or Koopman operator play...
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Variational Koopman models: slow collective variables and molecular kinetics from short offequilibrium simulations
Markov state models (MSMs) and Master equation models are popular approa...
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A Traveling Salesman Learns Bayesian Networks
Structure learning of Bayesian networks is an important problem that ari...
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Stefan Klus
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