83 citations · 332 across the 27 of their papers we have counts for
4 papers · 1 filter
A Faster Sampler for Discrete Determinantal Point Processes
Simon Barthelmé, Nicolas Tremblay, Pierre-Olivier Amblard
Discrete Determinantal Point Processes (DPPs) have a wide array of potential applications for subsampling datasets. They are however held back in some cases by the high cost of sam…
Smoothing complex-valued signals on Graphs with Monte-Carlo
Hugo Jaquard, Michaël Fanuel, Pierre-Olivier Amblard +3
We introduce new smoothing estimators for complex signals on graphs, based on a recently studied Determinantal Point Process (DPP). These estimators are built from subsets of edges…
Variance Reduction for Inverse Trace Estimation via Random Spanning Forests
Yusuf Yigit Pilavci, Pierre-Olivier Amblard, Simon Barthelme +1
The trace $\tr(q(\ma{L} + q\ma{I})^{-1})$, where $\ma{L}$ is a symmetric diagonally dominant matrix, is the quantity of interest in some machine learning problems. However, its dir…
Gaussian Process Regression in the Flat Limit
Simon Barthelmé, Pierre-Olivier Amblard, Nicolas Tremblay +1
Gaussian process (GP) regression is a fundamental tool in Bayesian statistics. It is also known as kriging and is the Bayesian counterpart to the frequentist kernel ridge regressio…