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Gaussian Process Learning for Power Systems
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Classroom Contents
Kernel Methods for Machine Learning and Dynamical Systems
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- 1 Approximation of matrix-valued functions with applications to Contraction Metrics
- 2 A Kernel Two-Sample Test For Functional Data
- 3 Gaussian Processes for Time Series Forecasting
- 4 The Random Feature Model for Input-Output Maps Between Function Spaces
- 5 Kernel Analog Forecasting for Multiscale Problems
- 6 Variably Scaled Discontinuous Kernels (VSDK): basics and some applications (Part 1 of 3 talks)
- 7 Variably Scaled Discontinuous Kernels (VSDK): basics and some applications (Part 2 of 3 talks)
- 8 Variably Scaled Discontinuous Kernels (VSDK): basics and some applications (Part 3 of 3 talks)
- 9 Learning patterns with kernels and learning kernels from patterns
- 10 Learning Model Parameters with an Unknown Observation Function
- 11 Gaussian Process Learning for Power Systems
- 12 Kernel Flows Demystified: Application to Regression
- 13 Consistency of Hierarchical Parameter Learning: Empirical Bayes and Kernel Flow Approaches
- 14 Sparse Cholesky factorization by Kullback-Leibler minimization
