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Title: Localizing and Amortizing: Efficient Inference for Gaussian Processes
The inference of Gaussian Processes concerns the distribution of the underlying function given observed data points. GP inference based on local ranges of data points is able to capture fine-scale correlations and allow fine-grained decomposition of the computation. Following this direction, we propose a new inference model that considers the correlations and observations of the K nearest neighbors for the inference at a data point. Compared with previous works, we also eliminate the data ordering prerequisite to simplify the inference process. Additionally, the inference task is decomposed to small subtasks with several technique innovations, making our model well suits the stochastic optimization. Since the decomposed small subtasks have the same structure, we further speed up the inference procedure with amortized inference. Our model runs efficiently and achieves good performances on several benchmark tasks.
Authors:
;
Award ID(s):
1850358
Publication Date:
NSF-PAR ID:
10253165
Journal Name:
Proceedings of The 12th Asian Conference on Machine Learning
Volume:
129
Page Range or eLocation-ID:
823 - 836
Sponsoring Org:
National Science Foundation
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