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05/19/2023
Transfer operators on graphs: Spectral clustering and beyond
Graphs and networks play an important role in modeling and analyzing com...
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04/06/2022
Koopman-based spectral clustering of directed and time-evolving graphs
While spectral clustering algorithms for undirected graphs are well esta...
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02/26/2022
A Dynamic Mode Decomposition Approach for Decentralized Spectral Clustering of Graphs
We propose a novel robust decentralized graph clustering algorithm that ...
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10/28/2021
Deeptime: a Python library for machine learning dynamical models from time series data
Generation and analysis of time-series data is relevant to many quantita...
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03/31/2021
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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12/14/2020
Data-driven model reduction of agent-based systems using the Koopman generator
The dynamical behavior of social systems can be described by agent-based...
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11/25/2020
Feature space approximation for kernel-based supervised learning
We propose a method for the approximation of high- or even infinite-dime...
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08/12/2020
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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05/27/2020
Kernel-based approximation of the Koopman generator and Schrödinger operator
Many dimensionality and model reduction techniques rely on estimating do...
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04/02/2020
Kernel autocovariance operators of stationary processes: Estimation and convergence
We consider autocovariance operators of a stationary stochastic process ...
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10/04/2019
Tensor-based algorithms for image classification
The interest in machine learning with tensor networks has been growing r...
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09/23/2019
Data-driven approximation of the Koopman generator: Model reduction, system identification, and control
We derive a data-driven method for the approximation of the Koopman gene...
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08/12/2019
Tensor-based EDMD for the Koopman analysis of high-dimensional systems
Recent years have seen rapid advances in the data-driven analysis of dyn...
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05/27/2019
Kernel Conditional Density Operators
We introduce a conditional density estimation model termed the condition...
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04/18/2019
A kernel-based method for coarse graining complex dynamical systems
We present a novel kernel-based machine learning algorithm for identifyi...
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04/16/2019
Kernel canonical correlation analysis approximates operators for the detection of coherent structures in dynamical data
We illustrate relationships between classical kernel-based dimensionalit...
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09/28/2018
A kernel-based approach to molecular conformation analysis
We present a novel machine learning approach to understanding conformati...
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07/24/2018
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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12/05/2017
Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces
Transfer operators such as the Perron-Frobenius or Koopman operator play...
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10/20/2016
Variational Koopman models: slow collective variables and molecular kinetics from short off-equilibrium simulations
Markov state models (MSMs) and Master equation models are popular approa...
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11/20/2012