148 citations · 1.1k across the 40 of their papers we have counts for
7 papers · 1 filter
Signal reconstruction using determinantal sampling
Ayoub Belhadji, Rémi Bardenet, Pierre Chainais
We study the approximation of a square-integrable function from a finite number of evaluations on a random set of nodes according to a well-chosen distribution. This is particularl…
Determinantal point processes based on orthogonal polynomials for sampling minibatches in SGD
Remi Bardenet, Subhro Ghosh, Meixia Lin
Stochastic gradient descent (SGD) is a cornerstone of machine learning. When the number N of data items is large, SGD relies on constructing an unbiased estimator of the gradient o…
Learning from DPPs via Sampling: Beyond HKPV and symmetry
Rémi Bardenet, Subhroshekhar Ghosh
Determinantal point processes (DPPs) have become a significant tool for recommendation systems, feature selection, or summary extraction, harnessing the intrinsic ability of these…
Kernel interpolation with continuous volume sampling
Ayoub Belhadji, Rémi Bardenet, Pierre Chainais
A fundamental task in kernel methods is to pick nodes and weights, so as to approximate a given function from an RKHS by the weighted sum of kernel translates located at the nodes.…
Kernel quadrature with DPPs
Ayoub Belhadji, Rémi Bardenet, Pierre Chainais
We study quadrature rules for functions from an RKHS, using nodes sampled from a determinantal point process (DPP). DPPs are parametrized by a kernel, and we use a truncated and sa…
A determinantal point process for column subset selection
Ayoub Belhadji, Rémi Bardenet, Pierre Chainais
Dimensionality reduction is a first step of many machine learning pipelines. Two popular approaches are principal component analysis, which projects onto a small number of well cho…