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Found 7 papers, 2 papers with code
Node Regression on Latent Position Random Graphs via Local Averaging
We show that in Latent Position Models this estimator tends to a Nadaraya Watson estimator in the latent space, and that its rate of convergence is in fact the same.
A Faster Sampler for Discrete Determinantal Point Processes
Finally, an interesting by-product of the analysis is that a realisation from a DPP is typically contained in a subset of size O(m log m) formed using leverage score i. i. d.
Variance reduction in stochastic methods for large-scale regularised least-squares problems
Large dimensional least-squares and regularised least-squares problems are expensive to solve.
Determinantal Point Processes for Coresets
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.
Asymptotic Equivalence of Fixed-size and Varying-size Determinantal Point Processes
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.
Graph sampling with determinantal processes
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.
The Poisson transform for unnormalised statistical models
Here we show that inferring the parameters of a unnormalised model on a space $\Omega$ can be mapped onto an equivalent problem of estimating the intensity of a Poisson point process on $\Omega$.
