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Found 11 papers, 3 papers with code
Split Covariance Intersection with Correlated Components for Distributed Estimation
Instead, a solution is to perform a conservative fusion.
Pseudorange Rigidity and Solvability of Cooperative GNSS Positioning
GNSS positioning relies on pseudorange measurements from satellites to receivers.
Fusion of Distance Measurements between Agents with Unknown Correlations
To optimally integrate a distance measurement between two users into a navigation filter, the correlation between the errors of their estimates must be known.
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 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.
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.
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.
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.
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.
