CUED Publications database

Automatic construction and natural-language description of nonparametric regression models

Lloyd, JR and Duvenaud, D and Grosse, R and Tenenbaum, JB and Ghahramani, Z (2014) Automatic construction and natural-language description of nonparametric regression models. Proceedings of the National Conference on Artificial Intelligence, 2. pp. 1242-1250.

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Copyright © 2014, Association for the Advancement of Artificial Intelligence ( All rights reserved. This paper presents the beginnings of an automatic statistician, focusing on regression problems. Our system explores an open-ended space of statistical models to discover a good explanation of a data set, and then produces a detailed report with figures and natural- language text. Our approach treats unknown regression functions non- parametrically using Gaussian processes, which has two important consequences. First, Gaussian processes can model functions in terms of high-level properties (e.g. smoothness, trends, periodicity, changepoints). Taken together with the compositional structure of our language of models this allows us to automatically describe functions in simple terms. Second, the use of flexible nonparametric models and a rich language for composing them in an open-ended manner also results in state- of-the-art extrapolation performance evaluated over 13 real time series data sets from various domains.

Item Type: Article
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:45
Last Modified: 24 May 2018 02:10