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.
Paper estimates GMMs with unknown covariances using sparse regularization.
Paper advances sparse regularisation theory for measures with new kernel insights.
This paper investigates the statistical estimation of a discrete mixing measure 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 0 in a grid-less manner from a sample of …
FS&P uses birth-death process to ensure global convergence of stochastic conic particle gradient descent.
A central question in modern machine learning and imaging sciences is to quantify the number of effective parameters of vastly over-parameterized models. The degrees of freedom is a mathematically convenient way to define this number of parameters. Its computation and properties are well understood when dealing with di…