Automatic Bayesian Density Analysis

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

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review


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.
Originele taal-2Engels
TitelProceedings of The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19)
UitgeverijAAAI Press
Aantal pagina's8
StatusGepubliceerd - 2019
Evenement33rd AAAI Conference on Artificial Intelligence - Hawaii, Honolulu, Verenigde Staten van Amerika
Duur: 27 jan 20191 feb 2019
Congresnummer: 33


Congres33rd AAAI Conference on Artificial Intelligence
Verkorte titelAAAI-19
LandVerenigde Staten van Amerika
Internet adres

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