Bridging algorithmic information theory and machine learning: a new approach to kernel learning
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Publication:6558847
DOI10.1016/j.physd.2024.134153MaRDI QIDQ6558847
Boumediene Hamzi, Houman Owhadi, Marcus Hutter
Publication date: 21 June 2024
Published in: Physica D (Search for Journal in Brave)
algorithmic information theorycompressionregressionmachine learningminimum description length principlesparse kernel flows
Artificial intelligence (68Txx) Inference from stochastic processes (62Mxx) Approximation methods and numerical treatment of dynamical systems (37Mxx)
Cites Work
- Unnamed Item
- A philosophical treatise of universal induction
- Explicativity, corroboration, and the relative odds of hypotheses. With comments by William L. Harper and John R. Wettersten
- Kernel methods for the approximation of some key quantities of nonlinear systems
- Dimensionality reduction of complex metastable systems via kernel embeddings of transition manifolds
- Kernel methods for center manifold approximation and a weak data-based version of the center manifold theorem
- Learning dynamical systems from data: a simple cross-validation perspective. I: Parametric kernel flows
- Learning ``best kernels from data in Gaussian process regression. With application to aerodynamics
- Data-driven approximation of the Koopman generator: model reduction, system identification, and control
- Operator-theoretic framework for forecasting nonlinear time series with kernel analog techniques
- Kernel flows: from learning kernels from data into the abyss
- Learning dynamical systems from data: a simple cross-validation perspective. III: Irregularly-sampled time series
- A note on microlocal kernel design for some slow-fast stochastic differential equations with critical transitions and application to EEG signals
- On the mathematical foundations of learning
- 9 Kernel methods for surrogate modeling
- Support Vector Machines
- Asymptotics of Discrete MDL for Online Prediction
- Local error estimates for radial basis function interpolation of scattered data
- A note on kernel methods for multiscale systems with critical transitions
- 10.1162/1532443041827952
- Operator-Adapted Wavelets, Fast Solvers, and Numerical Homogenization
- Bayesian Numerical Homogenization
- Kernel Methods for the Approximation of Nonlinear Systems
- The Loss Rank Principle for Model Selection
- Theory of Reproducing Kernels
- Learning dynamical systems from data: a simple cross-validation perspective. IV: Case with partial observations
- Information Theory With Kernel Methods
- Learning dynamical systems from data: a simple cross-validation perspective. V: Sparse kernel flows for 132 chaotic dynamical systems
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