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Found 4 papers, 1 papers with code
Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning
The resulting method is based on sparse spectrum Gaussian processes, enabling closed-form solutions, and is extensible to a stacked construction to capture more complex patterns.
Online Domain Adaptation for Occupancy Mapping
Further, with the use of high-fidelity driving simulators and real-world datasets, we demonstrate how parameters of 2D and 3D occupancy maps can be automatically adapted to accord with local spatial changes.
Optimal Transport for Distribution Adaptation in Bayesian Hilbert Maps
Parameters are one of the most critical components of machine learning models.
Index Set Fourier Series Features for Approximating Multi-dimensional Periodic Kernels
Periodicity is often studied in timeseries modelling with autoregressive methods but is less popular in the kernel literature, particularly for higher dimensional problems such as in textures, crystallography, and quantum mechanics.
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