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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,657 papers · 148 categories

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4 results for “Samplets”

Samplet basis pursuit: Multiresolution scattered data approximation with sparsity constraintsstat.ML

This paper approximates scattered data using samplet coordinates with sparsity constraints.

problem Scattered data approximation with sparsity constraints.

method Samplet basis pursuit with

\ell_1

-regularization, multiresolution techniques, and semi-smooth Newton method.

result The proposed method provides faster convergence and better signal sparsity compared to existing methods.

scattered data samplet basis sparsity constraints multiresolution semi-smooth Newton method

2023-06-16abs ↗pdf ↗

Samplet limits and multiwaveletsmath.NA

Samplets and multiwavelets constructed from scattered data converge to specific densities in the limit.

problem Constructing data-adapted multiresolution analyses and multiwavelets with flexible vanishing moments.

method Probabilistic framework for samplet construction; convergence to multiwavelets with broken polynomial densities.

result Samplet construction converges to multiwavelets in the infinite data limit.

samplets multiwavelets scattered data vanishing moments polynomial densities

2026-04-02abs ↗pdf ↗

Constructing Gaussian Processes via Sampletsstat.ML

This thesis tackles Gaussian Process challenges in low dimensions.

problem Constructing models for large datasets and selecting optimal models.

method Samplet-based approach to efficiently construct and train Gaussian Processes.

result Reduces cubic computational complexity to log-linear scale.

Gaussian Processes Samplets Optimal Regression Low-dimensional

2024-11-11abs ↗pdf ↗

On Quasi-Localized Dual Pairs in Reproducing Kernel Hilbert Spacesmath.NA

The study explores various localized bases and their duals for scattered data approximation.

problem Scattered data approximation using radial basis functions.

method Examines different localized bases including Lagrange, Newton, and multiresolution versions, and their duals.

result Localized orthogonal bases, such as the Newton basis, offer symmetric preconditioners and are feasible for scattered data approximation.

scattered data approximation radial basis functions localized bases symmetric preconditioners Newton basis

2024-08-21abs ↗pdf ↗