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10/31/2022
A Faster Sampler for Discrete Determinantal Point Processes
Discrete Determinantal Point Processes (DPPs) have a wide array of poten...
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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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01/04/2022
Gaussian Process Regression in the Flat Limit
Gaussian process (GP) regression is a fundamental tool in Bayesian stati...
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07/15/2021
Determinantal Point Processes in the Flat Limit
Determinantal point processes (DPPs) are repulsive point processes where...
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07/12/2021
Extended L-ensembles: a new representation for Determinantal Point Processes
Determinantal point processes (DPPs) are a class of repulsive point proc...
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11/20/2020
Graph Tikhonov Regularization and Interpolation via Random Spanning Forests
Novel Monte Carlo estimators are proposed to solve both the Tikhonov reg...
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10/30/2019
Spectral properties of kernel matrices in the flat limit
Kernel matrices are of central importance to many applied fields. In thi...
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10/17/2019
Smoothing graph signals via random spanning forests
Another facet of the elegant link between random processes on graphs and...
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05/06/2019
Estimating the inverse trace using random forests on graphs
Some data analysis problems require the computation of (regularised) inv...
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03/23/2018
Determinantal Point Processes for Coresets
When one is faced with a dataset too large to be used all at once, an ob...
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03/05/2018
Asymptotic Equivalence of Fixed-size and Varying-size Determinantal Point Processes
Determinantal Point Processes (DPPs) are popular models for point proces...
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02/23/2018
Optimized Algorithms to Sample Determinantal Point Processes
In this technical report, we discuss several sampling algorithms for Det...
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03/05/2017
Graph sampling with determinantal processes
We present a new random sampling strategy for k-bandlimited signals defi...
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07/29/2011
