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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.

168,742 papers · 148 categories

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5 results for “Beurling-LASSO”

Gaussian Mixture Model with unknown diagonal covariances via continuous sparse regularizationmath.ST

Paper estimates GMMs with unknown covariances using sparse regularization.

problem Estimating GMMs with unknown diagonal covariances from samples.

method Employed Beurling-LASSO (BLASSO) for sparse estimation of component means, covariances, and weights.

result Established non-asymptotic recovery guarantees with nearly parametric convergence rates.

Gaussian Mixture Models Sparse Estimation Beurling-LASSO Diagonal Covariances Non-degenerate Dual Certificates

2025-09-16abs ↗pdf ↗

Effective regions and kernels in continuous sparse regularisation, with application to sketched mixturesmath.ST

Paper advances sparse regularisation theory for measures with new kernel insights.

problem Estimating sparse measures from noisy observations using continuous sparse regularisation.

method Develops new continuous sparse regularisation theory on measures with Beurling-LASSO, introduces kernel switch analysis.

result Proves the ``sinc-4'' kernel satisfies a technical LPC assumption for error bounds.

sparse regularisation Beurling-LASSO kernel analysis mixtures sketching

2025-07-11abs ↗pdf ↗

SuperMix: Sparse Regularization for Mixturesmath.ST

This paper investigates the statistical estimation of a discrete mixing measure $μ$ 0 involved in a kernel mixture model. Using some recent advances in l1-regularization over the space of measures, we introduce a "data fitting and regularization" convex program for estimating $μ$ 0 in a grid-less manner from a sample of …

2019-07-23abs ↗pdf ↗

Fast Spawn\&Prune (FS\&P): Global convergence of stochastic conic particle gradient descent via birth/death processmath.OC

FS&P uses birth-death process to ensure global convergence of stochastic conic particle gradient descent.

problem Global optimization of non-convex objective functions over measure space.

method Introduces Fast Spawn\&Prune (FS\&P) combining CPGD with birth-death process.

result First theoretical guarantee of global convergence for discrete-time stochastic algorithms.

Global Optimization Conic Particle Gradient Descent Birth-Death Process Continuous Sparse Regression Excess Risk

2026-05-19abs ↗pdf ↗

Degrees of freedom for off-the-grid sparse estimationstat.ML

New method calculates degrees of freedom for sparse estimation in continuous models.

problem Quantifying effective parameters in over-parameterized models with large continuous parameter spaces.

method Develops a continuous Lasso method for sparsity-inducing optimization over measure spaces.

result Proof of a continuous degrees of freedom formula for Beurling Lasso.

degrees of freedom sparse estimation convex optimization continuous models super-resolution

2019-11-08abs ↗pdf ↗