Summary of Adaptive Rkhs Fourier Features For Compositional Gaussian Process Models, by Xinxing Shi et al.
Adaptive RKHS Fourier Features for Compositional Gaussian Process Models
by Xinxing Shi, Thomas Baldwin-McDonald, Mauricio A. Álvarez
First submitted to arxiv on: 1 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach in Gaussian Process modeling is introduced, which extends the use of global Fourier features to compositional GPs involving linear transformations. The proposed method incorporates Ordinary Differential Equation (ODE)-based Reproducing Kernel Hilbert Space (RKHS) Fourier features that enable adaptive amplitude and phase modulation through convolution operations. This convolutional formulation relates our work to deep latent force models designed for modeling nonlinear dynamical systems. A doubly stochastic variational inference framework is employed, leading to improved predictive performance across various regression tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Gaussian Processes are a type of machine learning model that can be used to make predictions about continuous outcomes. This paper introduces a new way to improve these models by adding features that capture complex patterns in the data. The new features are based on ordinary differential equations, which are used to describe how things change over time. These features allow for more flexible and adaptive modeling of non-stationary processes. The approach is shown to work well for regression tasks. |
Keywords
» Artificial intelligence » Inference » Machine learning » Regression
