13 citations · 23 across the 12 of their papers we have counts for
12 papers
To discretize continually: Mean shift interacting particle systems for Bayesian inference
Ayoub Belhadji, Daniel Sharp, Youssef M. Marzouk
Integration against a probability distribution given its unnormalized density is a central task in Bayesian inference and other fields. We introduce new methods for approximating s…
One Operator for Many Densities: Amortized Approximation of Conditioning by Neural Operators
Panos Tsimpos, Edoardo Calvello, Ayoub Belhadji +1
Probabilistic conditioning is concerned with the identification of a distribution of a random variable given a random variable . It is a cornerstone of scientific and engine…
Weighted quantization using MMD: From mean field to mean shift via gradient flows
Ayoub Belhadji, Daniel Sharp, Youssef Marzouk
Approximating a probability distribution using a set of particles is a fundamental problem in machine learning and statistics, with applications including clustering and quantizati…
On the design of scalable, high-precision spherical-radial Fourier features
Ayoub Belhadji, Qianyu Julie Zhu, Youssef Marzouk
Approximation using Fourier features is a popular technique for scaling kernel methods to large-scale problems, with myriad applications in machine learning and statistics. This me…
Nonlinear Bayesian optimal experimental design using logarithmic Sobolev inequalities
Fengyi Li, Ayoub Belhadji, Youssef Marzouk
We study the problem of selecting experiments from a larger candidate pool, where the goal is to maximize mutual information (MI) between the selected subset and the underlying…
Sketch and shift: a robust decoder for compressive clustering
Ayoub Belhadji, Rémi Gribonval
Compressive learning is an emerging approach to drastically reduce the memory footprint of large-scale learning, by first summarizing a large dataset into a low-dimensional sketch…
Revisiting RIP guarantees for sketching operators on mixture models
Ayoub Belhadji, Rémi Gribonval
In the context of sketching for compressive mixture modeling, we revisit existing proofs of the Restricted Isometry Property of sketching operators with respect to certain mixtures…
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…
An analysis of Ermakov-Zolotukhin quadrature using kernels
Ayoub Belhadji
We study a quadrature, proposed by Ermakov and Zolotukhin in the sixties, through the lens of kernel methods. The nodes of this quadrature rule follow the distribution of a determi…
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…