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Deep Kernel For GPs.pdf

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Preview of Deep Kernel for GPs
🔗 Source: auai.org
📊 Size: 3.04 MB
📄 Pages: 10 pages
⬇️ Downloads: 506

Summary

The proposed Gaussian process kernel utilizes a deep neural network (DNN) structure while maintaining good interpretability. It addresses four major issues: optimality, explainability, model complexity, and sample efficiency. The kernel design involves three steps:

1. Derivation of an optimal kernel with a non-stationary dot product structure that minimizes the prediction/test mean-squared-error (MSE).
2. Decomposition of this optimal kernel as a linear combination of shallow DNN subnetworks with the aid of multi-way feature interaction detection.
3. Updating the hyperparameters of the subnetworks via an alternating rationale until convergence.

The designed kernel does not sacrifice interpretability for optimality, as each subnetwork explicitly demonstrates the interaction of a set of features in a transformation function. The proposed kernel is tested with both synthesized and real-world datasets and shows superior prediction performance in most cases, while maintaining robustness to data overfitting issues when reducing the number of samples.

Key contributions include the derivation of a non-stationary optimal kernel function that minimizes the test MSE and the decomposition of the NN structure into a linear combination of shallow subnetworks with feature interaction detection, which is a research frontier towards explainable AI.

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Description

A novel Gaussian process kernel is proposed, leveraging a deep neural network structure while maintaining interpretability. It addresses issues of optimality, explainability, model complexity, and sample efficiency. The kernel design involves deriving an optimal non-stationary dot product structure.

Technical Information

  • File Format: PDF
  • File Size: 3.04 MB
  • Pages: 10
  • Language: EN
  • Total Downloads: 506
  • Last Updated: 19 hours ago

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