Scalable Structure Discovery in Regression using Gaussian Processes

Hyunjik Kim, Yee Whye Teh
Proceedings of the Workshop on Automatic Machine Learning, PMLR 64:31-40, 2016.

Abstract

Automatic Bayesian Covariance Discovery (ABCD) in Lloyd et. al (2014) provides a framework for automating statistical modelling as well as exploratory data analysis for regression problems. However ABCD does not scale due to its $O(N^3)$ running time. This is undesirable not only because the average size of data sets is growing fast, but also because there is potentially more information in bigger data, implying a greater need for more expressive models that can discover sophisticated structure. We propose a scalable version of ABCD, to encompass big data within the boundaries of automated statistical modelling.

Cite this Paper

BibTeX
@InProceedings{pmlr-v64-kim_scalable_2016, title = {Scalable Structure Discovery in Regression using Gaussian Processes}, author = {Kim, Hyunjik and Teh, Yee Whye}, booktitle = {Proceedings of the Workshop on Automatic Machine Learning}, pages = {31--40}, year = {2016}, editor = {Hutter, Frank and Kotthoff, Lars and Vanschoren, Joaquin}, volume = {64}, series = {Proceedings of Machine Learning Research}, address = {New York, New York, USA}, month = {24 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v64/kim_scalable_2016.pdf}, url = {https://proceedings.mlr.press/v64/kim_scalable_2016.html}, abstract = {Automatic Bayesian Covariance Discovery (ABCD) in Lloyd et. al (2014) provides a framework for automating statistical modelling as well as exploratory data analysis for regression problems. However ABCD does not scale due to its $O(N^3)$ running time. This is undesirable not only because the average size of data sets is growing fast, but also because there is potentially more information in bigger data, implying a greater need for more expressive models that can discover sophisticated structure. We propose a scalable version of ABCD, to encompass big data within the boundaries of automated statistical modelling.} }
Endnote
%0 Conference Paper %T Scalable Structure Discovery in Regression using Gaussian Processes %A Hyunjik Kim %A Yee Whye Teh %B Proceedings of the Workshop on Automatic Machine Learning %C Proceedings of Machine Learning Research %D 2016 %E Frank Hutter %E Lars Kotthoff %E Joaquin Vanschoren %F pmlr-v64-kim_scalable_2016 %I PMLR %P 31--40 %U https://proceedings.mlr.press/v64/kim_scalable_2016.html %V 64 %X Automatic Bayesian Covariance Discovery (ABCD) in Lloyd et. al (2014) provides a framework for automating statistical modelling as well as exploratory data analysis for regression problems. However ABCD does not scale due to its $O(N^3)$ running time. This is undesirable not only because the average size of data sets is growing fast, but also because there is potentially more information in bigger data, implying a greater need for more expressive models that can discover sophisticated structure. We propose a scalable version of ABCD, to encompass big data within the boundaries of automated statistical modelling.
RIS
TY - CPAPER TI - Scalable Structure Discovery in Regression using Gaussian Processes AU - Hyunjik Kim AU - Yee Whye Teh BT - Proceedings of the Workshop on Automatic Machine Learning DA - 2016/12/04 ED - Frank Hutter ED - Lars Kotthoff ED - Joaquin Vanschoren ID - pmlr-v64-kim_scalable_2016 PB - PMLR DP - Proceedings of Machine Learning Research VL - 64 SP - 31 EP - 40 L1 - http://proceedings.mlr.press/v64/kim_scalable_2016.pdf UR - https://proceedings.mlr.press/v64/kim_scalable_2016.html AB - Automatic Bayesian Covariance Discovery (ABCD) in Lloyd et. al (2014) provides a framework for automating statistical modelling as well as exploratory data analysis for regression problems. However ABCD does not scale due to its $O(N^3)$ running time. This is undesirable not only because the average size of data sets is growing fast, but also because there is potentially more information in bigger data, implying a greater need for more expressive models that can discover sophisticated structure. We propose a scalable version of ABCD, to encompass big data within the boundaries of automated statistical modelling. ER -
APA
Kim, H. & Teh, Y.W.. (2016). Scalable Structure Discovery in Regression using Gaussian Processes. Proceedings of the Workshop on Automatic Machine Learning, in Proceedings of Machine Learning Research 64:31-40 Available from https://proceedings.mlr.press/v64/kim_scalable_2016.html.

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