Corpus ID: 3308620

Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes

@inproceedings{Kim2018ScalingUT,
  title={Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes},
  author={Hyunjik Kim and Y. Teh},
  booktitle={AISTATS},
  year={2018}
}
Automating statistical modelling is a challenging problem in artificial intelligence. The Automatic Statistician takes a first step in this direction, by employing a kernel search algorithm with Gaussian Processes (GP) to provide interpretable statistical models for regression problems. However this does not scale due to its $O(N^3)$ running time for the model selection. We propose Scalable Kernel Composition (SKC), a scalable kernel search algorithm that extends the Automatic Statistician to… Expand
19 Citations
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