Search Results for author: Pierre-Olivier Amblard

Found 5 papers, 0 papers with code

Determinantal Point Processes for Coresets

no code implementations23 Mar 2018 Nicolas Tremblay, Simon Barthelmé, Pierre-Olivier Amblard

We apply our results to both the k-means and the linear regression problems, and give extensive empirical evidence that the small additional computational cost of DPP sampling comes with superior performance over its iid counterpart.

Point Processes

Asymptotic Equivalence of Fixed-size and Varying-size Determinantal Point Processes

no code implementations5 Mar 2018 Simon Barthelmé, Pierre-Olivier Amblard, Nicolas Tremblay

In this work we show that as the size of the ground set grows, $k$-DPPs and DPPs become equivalent, meaning that their inclusion probabilities converge.

Point Processes

Optimized Algorithms to Sample Determinantal Point Processes

no code implementations23 Feb 2018 Nicolas Tremblay, Simon Barthelme, Pierre-Olivier Amblard

The standard sampling algorithm is separated in three phases: 1/~eigendecomposition of $\mathbf{L}$, 2/~an eigenvector sampling phase where $\mathbf{L}$'s eigenvectors are sampled independently via a Bernoulli variable parametrized by their associated eigenvalue, 3/~a Gram-Schmidt-type orthogonalisation procedure of the sampled eigenvectors.

Point Processes

Échantillonnage de signaux sur graphes via des processus déterminantaux

no code implementations7 Apr 2017 Nicolas Tremblay, Simon Barthelme, Pierre-Olivier Amblard

We consider the problem of sampling k-bandlimited graph signals, ie, linear combinations of the first k graph Fourier modes.

Point Processes

Graph sampling with determinantal processes

no code implementations5 Mar 2017 Nicolas Tremblay, Pierre-Olivier Amblard, Simon Barthelmé

For large graphs, ie, in cases where the graph's spectrum is not accessible, we investigate, both theoretically and empirically, a sub-optimal but much faster DPP based on loop-erased random walks on the graph.

Graph Sampling Point Processes

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