Rémi Bardenet | DeepAI

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∙ 08/09/2023

Repelled point processes with application to numerical integration

Linear statistics of point processes yield Monte Carlo estimators of int...

0 Diala Hawat, et al. ∙

∙ 05/25/2023

On sampling determinantal and Pfaffian point processes on a quantum computer

DPPs were introduced by Macchi as a model in quantum optics the 1970s. S...

0 Rémi Bardenet, et al. ∙

∙ 10/15/2022

Smoothing complex-valued signals on Graphs with Monte-Carlo

We introduce new smoothing estimators for complex signals on graphs, bas...

0 Hugo Jaquard, et al. ∙

∙ 08/31/2022

Sparsification of the regularized magnetic Laplacian with multi-type spanning forests

In this paper, we consider a U(1)-connection graph, that is, a graph whe...

0 Michaël Fanuel, et al. ∙

∙ 03/16/2022

On estimating the structure factor of a point process, with applications to hyperuniformity

Hyperuniformity is the study of stationary point processes with a sub-Po...

0 Diala Hawat, et al. ∙

∙ 02/08/2022

A covariant, discrete time-frequency representation tailored for zero-based signal detection

Recent work in time-frequency analysis proposed to switch the focus from...

0 Barbara Pascal, et al. ∙

∙ 12/11/2021

Determinantal point processes based on orthogonal polynomials for sampling minibatches in SGD

Stochastic gradient descent (SGD) is a cornerstone of machine learning. ...

0 Rémi Bardenet, et al. ∙

∙ 06/27/2021

Nonparametric estimation of continuous DPPs with kernel methods

Determinantal Point Process (DPPs) are statistical models for repulsive ...

5 Michaël Fanuel, et al. ∙

∙ 11/09/2020

On proportional volume sampling for experimental design in general spaces

Optimal design for linear regression is a fundamental task in statistics...

0 Arnaud Poinas, et al. ∙

∙ 07/08/2020

Learning from DPPs via Sampling: Beyond HKPV and symmetry

Determinantal point processes (DPPs) have become a significant tool for ...

0 Rémi Bardenet, et al. ∙

∙ 03/04/2020

Fast sampling from β-ensembles

We study sampling algorithms for β-ensembles with time complexity less t...

0 Guillaume Gautier, et al. ∙

∙ 02/22/2020

Kernel interpolation with continuous volume sampling

A fundamental task in kernel methods is to pick nodes and weights, so as...

0 Ayoub Belhadji, et al. ∙

∙ 06/18/2019

Kernel quadrature with DPPs

We study quadrature rules for functions living in an RKHS, using nodes s...

0 Ayoub Belhadji, et al. ∙

∙ 12/23/2018

A determinantal point process for column subset selection

Dimensionality reduction is a first step of many machine learning pipeli...

0 Ayoub Belhadji, et al. ∙

∙ 09/19/2018

DPPy: Sampling Determinantal Point Processes with Python

Determinantal point processes (DPPs) are specific probability distributi...

0 Guillaume Gautier, et al. ∙

∙ 07/30/2018

Time-frequency transforms of white noises and Gaussian analytic functions

A family of Gaussian analytic functions (GAFs) has recently been linked ...

0 Rémi Bardenet, et al. ∙

∙ 05/30/2017

Zonotope hit-and-run for efficient sampling from projection DPPs

Determinantal point processes (DPPs) are distributions over sets of item...

0 Guillaume Gautier, et al. ∙

∙ 07/04/2015

Inference for determinantal point processes without spectral knowledge

Determinantal point processes (DPPs) are point process models that natur...

0 Rémi Bardenet, et al. ∙

∙ 05/11/2015

On Markov chain Monte Carlo methods for tall data

Markov chain Monte Carlo methods are often deemed too computationally in...

0 Rémi Bardenet, et al. ∙

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