Discrete Determinantal Point Processes (DPPs) have a wide array of poten...
We introduce new smoothing estimators for complex signals on graphs, bas...
Gaussian process (GP) regression is a fundamental tool in Bayesian
stati...
Determinantal point processes (DPPs) are repulsive point processes where...
Determinantal point processes (DPPs) are a class of repulsive point
proc...
Novel Monte Carlo estimators are proposed to solve both the Tikhonov
reg...
Kernel matrices are of central importance to many applied fields. In thi...
Another facet of the elegant link between random processes on graphs and...
Some data analysis problems require the computation of (regularised) inv...
When one is faced with a dataset too large to be used all at once, an ob...
Determinantal Point Processes (DPPs) are popular models for point proces...
In this technical report, we discuss several sampling algorithms for
Det...
We present a new random sampling strategy for k-bandlimited signals defi...
Many models of interest in the natural and social sciences have no
close...