Search Results for author:
Found 14 papers, 4 papers with code
Simplicity bias, algorithmic probability, and the random logistic map
We discover that simplicity bias is observable in the random logistic map for specific ranges of $\mu$ and noise magnitudes.
Bridging Algorithmic Information Theory and Machine Learning: A New Approach to Kernel Learning
Machine Learning (ML) and Algorithmic Information Theory (AIT) look at Complexity from different points of view.
Learning Dynamical Systems from Data: A Simple Cross-Validation Perspective, Part V: Sparse Kernel Flows for 132 Chaotic Dynamical Systems
In this paper, we introduce the method of \emph{Sparse Kernel Flows } in order to learn the ``best'' kernel by starting from a large dictionary of kernels.
One-Shot Learning of Stochastic Differential Equations with Data Adapted Kernels
(2) Complete the graph (approximate unknown functions and random variables) via Maximum a Posteriori Estimation (given the data) with Gaussian Process (GP) priors on the unknown functions.
Learning dynamical systems from data: A simple cross-validation perspective, part III: Irregularly-Sampled Time Series
A simple and interpretable way to learn a dynamical system from data is to interpolate its vector-field with a kernel.
Data-driven geophysical forecasting: Simple, low-cost, and accurate baselines with kernel methods
Modeling geophysical processes as low-dimensional dynamical systems and regressing their vector field from data is a promising approach for learning emulators of such systems.
Kernel methods for center manifold approximation and a data-based version of the Center Manifold Theorem
We then use an apposite data-based kernel method to construct a suitable approximation of the manifold close to the equilibrium, which is compatible with our general error theory.
Learning dynamical systems from data: a simple cross-validation perspective
Regressing the vector field of a dynamical system from a finite number of observed states is a natural way to learn surrogate models for such systems.
Kernel-based approximation of the Koopman generator and Schrödinger operator
Furthermore, we exploit that, under certain conditions, the Schr\"odinger operator can be transformed into a Kolmogorov backward operator corresponding to a drift-diffusion process and vice versa.
Dimensionality Reduction of Complex Metastable Systems via Kernel Embeddings of Transition Manifolds
We present a novel kernel-based machine learning algorithm for identifying the low-dimensional geometry of the effective dynamics of high-dimensional multiscale stochastic systems.
A Note on Kernel Methods for Multiscale Systems with Critical Transitions
We study the maximum mean discrepancy (MMD) in the context of critical transitions modelled by fast-slow stochastic dynamical systems.
Kernel Methods for Linear Discrete-Time Equations
Methods from learning theory are used in the state space of linear dynamical and control systems in order to estimate the system matrices.
Kernel Methods for the Approximation of Some Key Quantities of Nonlinear Systems
We introduce a data-based approach to estimating key quantities which arise in the study of nonlinear control systems and random nonlinear dynamical systems.
Kernel Methods for the Approximation of Nonlinear Systems
We introduce a data-driven order reduction method for nonlinear control systems, drawing on recent progress in machine learning and statistical dimensionality reduction.
