Linear statistics of point processes yield Monte Carlo estimators of
int...
DPPs were introduced by Macchi as a model in quantum optics the 1970s. S...
We introduce new smoothing estimators for complex signals on graphs, bas...
In this paper, we consider a U(1)-connection graph, that is, a graph
whe...
Hyperuniformity is the study of stationary point processes with a sub-Po...
Recent work in time-frequency analysis proposed to switch the focus from...
Stochastic gradient descent (SGD) is a cornerstone of machine learning. ...
Determinantal Point Process (DPPs) are statistical models for repulsive ...
Optimal design for linear regression is a fundamental task in statistics...
Determinantal point processes (DPPs) have become a significant tool for
...
We study sampling algorithms for β-ensembles with time complexity less
t...
A fundamental task in kernel methods is to pick nodes and weights, so as...
We study quadrature rules for functions living in an RKHS, using nodes
s...
Dimensionality reduction is a first step of many machine learning pipeli...
Determinantal point processes (DPPs) are specific probability distributi...
A family of Gaussian analytic functions (GAFs) has recently been linked ...
Determinantal point processes (DPPs) are distributions over sets of item...
Determinantal point processes (DPPs) are point process models that natur...
Markov chain Monte Carlo methods are often deemed too computationally
in...