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- W2624473996 abstract "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 bigger data sets. In doing so, we derive a cheap upper bound on the GP marginal likelihood that sandwiches the marginal likelihood with the variational lower bound . We show that the upper bound is significantly tighter than the lower bound and thus useful for model selection." @default.
- W2624473996 created "2017-06-15" @default.
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- W2624473996 date "2017-06-08" @default.
- W2624473996 modified "2023-09-27" @default.
- W2624473996 title "Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes" @default.
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