Search Results for author:
Found 7 papers, 4 papers with code
Linear quadratic control of nonlinear systems with Koopman operator learning and the Nyström method
In this paper, we study how the Koopman operator framework can be combined with kernel methods to effectively control nonlinear dynamical systems.
Efficient Numerical Integration in Reproducing Kernel Hilbert Spaces via Leverage Scores Sampling
In this work we consider the problem of numerical integration, i. e., approximating integrals with respect to a target probability measure using only pointwise evaluations of the integrand.
Estimating Koopman operators with sketching to provably learn large scale dynamical systems
Our empirical and theoretical analysis shows that the proposed estimators provide a sound and efficient way to learn large scale dynamical systems.
M$^2$M: A general method to perform various data analysis tasks from a differentially private sketch
In this paper, we introduce the generic moment-to-moment (M$^2$M) method to perform a wide range of data exploration tasks from a single private sketch.
Nyström Kernel Mean Embeddings
Our main result is an upper bound on the approximation error of this procedure.
Mean Nyström Embeddings for Adaptive Compressive Learning
Compressive learning is an approach to efficient large scale learning based on sketching an entire dataset to a single mean embedding (the sketch), i. e. a vector of generalized moments.
Sketching Datasets for Large-Scale Learning (long version)
This article considers "compressive learning," an approach to large-scale machine learning where datasets are massively compressed before learning (e. g., clustering, classification, or regression) is performed.
