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Found 4 papers, 2 papers with code
Optimized Algorithms to Sample Determinantal Point Processes
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.
Visualizing the Effects of a Changing Distance on Data Using Continuous Embeddings
The right scale is hard to pin down and it is preferable when results do not depend too tightly on the exact value one picked.
Échantillonnage de signaux sur graphes via des processus déterminantaux
We consider the problem of sampling k-bandlimited graph signals, ie, linear combinations of the first k graph Fourier modes.
Variance Reduction for Inverse Trace Estimation via Random Spanning Forests
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.
