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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...
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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...
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10/15/2022
Smoothing complex-valued signals on Graphs with Monte-Carlo
We introduce new smoothing estimators for complex signals on graphs, bas...
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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...
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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...
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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...
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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. ...
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06/27/2021
Nonparametric estimation of continuous DPPs with kernel methods
Determinantal Point Process (DPPs) are statistical models for repulsive ...
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11/09/2020
On proportional volume sampling for experimental design in general spaces
Optimal design for linear regression is a fundamental task in statistics...
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07/08/2020
Learning from DPPs via Sampling: Beyond HKPV and symmetry
Determinantal point processes (DPPs) have become a significant tool for ...
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03/04/2020
Fast sampling from β-ensembles
We study sampling algorithms for β-ensembles with time complexity less t...
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02/22/2020
Kernel interpolation with continuous volume sampling
A fundamental task in kernel methods is to pick nodes and weights, so as...
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06/18/2019
Kernel quadrature with DPPs
We study quadrature rules for functions living in an RKHS, using nodes s...
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12/23/2018
A determinantal point process for column subset selection
Dimensionality reduction is a first step of many machine learning pipeli...
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09/19/2018
DPPy: Sampling Determinantal Point Processes with Python
Determinantal point processes (DPPs) are specific probability distributi...
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07/30/2018
Time-frequency transforms of white noises and Gaussian analytic functions
A family of Gaussian analytic functions (GAFs) has recently been linked ...
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05/30/2017
Zonotope hit-and-run for efficient sampling from projection DPPs
Determinantal point processes (DPPs) are distributions over sets of item...
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07/04/2015
Inference for determinantal point processes without spectral knowledge
Determinantal point processes (DPPs) are point process models that natur...
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05/11/2015
