Retrieval of experiments by efficient comparison of marginal likelihoods

Sohan Seth, John Shawe-Taylor, Samuel Kaski

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We study the task of retrieving relevant experiments given a query experiment. By experiment, we mean a collection of measurements from a set of ‘covariates’ and the associated ‘outcomes’. While similar experiments can be retrieved by comparing available ‘annotations’, this approach ignores the valuable information available in the measurements themselves. To incorporate this information in the retrieval task, we suggest employing a retrieval metric that utilizes probabilistic models learned from the measurements. We argue that such a metric is a sensible measure of similarity between two experiments since it permits inclusion of experiment-specific prior knowledge. However, accurate models are often not analytical, and one must resort to storing posterior samples which demands considerable resources. Therefore, we study strategies to select informative posterior samples to reduce the computational load while maintaining the retrieval performance. We demonstrate the efficacy of our approach on simulated data with simple linear regression as the models, and real world datasets.
Original languageEnglish
Title of host publicationNeural Information Processing
Subtitle of host publication21st International Conference, ICONIP 2014, Kuching, Malaysia, November 3-6, 2014. Proceedings, Part II
PublisherSpringer International Publishing
Pages135-142
Number of pages8
ISBN (Electronic)978-3-319-12640-1
ISBN (Print)978-3-319-12639-5
DOIs
Publication statusPublished - 3 Nov 2014

Publication series

NameLecture Notes in Computer Science
PublisherSpringer International Publishing
Volume8835
ISSN (Print)0302-9743

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