Automatic Bayesian Density Analysis

Antonio Vergari, Alejandro Molina, Robert Peharz, Zoubin Ghahramani, Kristian Kersting, Isabel Valera

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

Abstract / Description of output

Making sense of a dataset in an automatic and unsupervised fashion is a challenging problem in statistics and AI. Classical approaches for exploratory data analysis are usually not flexible enough to deal with the uncertainty inherent to real-world data: they are often restricted to fixed latent interaction models and homogeneous likelihoods; they are sensitive to missing, corrupt and anomalous data; moreover, their expressiveness generally comes at the price of intractable inference. As a result, supervision from statisticians is usually needed to find the right model for the data. However, since domain experts are not necessarily also experts in statistics, we propose Automatic Bayesian Density Analysis (ABDA) to make exploratory data analysis accessible at large. Specifically, ABDA allows for automatic and efficient missing value estimation, statistical data type and likelihood discovery, anomaly detection and dependency structure mining, on top of providing accurate density estimation. Extensive empirical evidence shows that ABDA is a suitable tool for automatic exploratory analysis of mixed continuous and discrete tabular data.
Original languageEnglish
Title of host publicationProceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19)
Subtitle of host publicationAAAI Technical Track: Machine Learning
Place of PublicationPalo Alto, California, USA
PublisherAAAI Press
Pages5207-5215
Number of pages9
Volume33
ISBN (Electronic)978-1-57735-809-1
DOIs
Publication statusPublished - 23 Jul 2019
EventThe Thirty-Third AAAI Conference on Artificial Intelligence - Hilton Hawaiian Village, Honolulu, Hawaii, United States
Duration: 27 Jan 20191 Feb 2019
https://aaai.org/Conferences/AAAI-19/

Publication series

NameProceedings of the AAAI Conference on Artificial Intelligence
PublisherAAAI Press
Number1
Volume33
ISSN (Print)2159-5399
ISSN (Electronic)2374-3468

Conference

ConferenceThe Thirty-Third AAAI Conference on Artificial Intelligence
Abbreviated titleAAAI 2019
Country/TerritoryUnited States
CityHonolulu, Hawaii
Period27/01/191/02/19
Internet address

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