Towards a Unified Quadrature Framework for Large-Scale Kernel Machines
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- Type
- preprint
- Published
- 2020-11-03
- Cited by
- 4
- References
- 66
- Access
- Open access
- OpenAlex
- https://openalex.org/W3097924754
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:226236996
Keywords
Gaussian quadrature, Mathematics, Kernel (algebra), Numerical integration, Variance reduction
References
- Practical Numerical Integration
- Introduction to Quasi-Monte Carlo Integration and Applications
- Least Squares Support Vector Machines
- Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
- Relations Between Sparse-Grid Quadrature Rule and Spherical-Radial Cubature Rule in Nonlinear Gaussian Estimation
- Likelihood approximation by numerical integration on sparse grids
- The spectrum of kernel random matrices
- Stochastic Integration Rules for Infinite Regions
- Random number generation and Quasi-Monte Carlo methods
- Cubature Kalman Filters
- Efficiency of Multivariate Control Variates in Monte Carlo Simulation
- Construction of fully symmetric numerical integration formulas of fully symmetric numerical integration formulas
- Simple Cubature Formulas with High Polynomial Exactness
- Fully symmetric interpolatory rules for multiple integrals over infinite regions with Gaussian weight
- Erratum to"Higher order scrambled digital nets achieve the optimal rate of the root mean square error for smooth integrands"
- Randomized Nonlinear Component Analysis
- Random Features for Large-Scale Kernel Machines
- Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels
- Monte Carlo and quasi-Monte Carlo methods
- Cubature formulas for symmetric measures in higher dimensions with few points
Cited by
- Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond
- Unlocking the Potential of Non-PSD Kernel Matrices: A Polar Decomposition-based Transformation for Improved Prediction Models
- Auxiliary two-filter particle smoothing for one generalized hidden Markov model.
- Fast learning in reproducing kernel Kre˘ın spaces via signed measures
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