
A Tool for Custom Construction of QMC and RQMC Point Sets
We present LatNet Builder, a software tool to find good parameters for l...
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Multilevel Monte Carlo learning
In this work, we study the approximation of expected values of functiona...
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MultiScale Process Modelling and Distributed Computation for Spatial Data
Recent years have seen a huge development in spatial modelling and predi...
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An algorithm to compute the tvalue of a digital net and of its projections
Digital nets are among the most successful methods to construct lowdisc...
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Neural Network Gradient Hamiltonian Monte Carlo
Hamiltonian Monte Carlo is a widely used algorithm for sampling from pos...
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Global consensus Monte Carlo
For Bayesian inference with large data sets, it is often convenient or n...
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Optimal Learning from the DoobDynkin lemma
The DoobDynkin Lemma gives conditions on two functions X and Y that ens...
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A quasiMonte Carlo data compression algorithm for machine learning
We introduce an algorithm to reduce large data sets using socalled digital nets, which are well distributed point sets in the unit cube. These point sets together with weights, which depend on the data set, are used to represent the data. We show that this can be used to reduce the computational effort needed in finding good parameters in machine learning algorithms. To illustrate our method we provide some numerical examples for neural networks.
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