Working with Deep Generative Models and Tabular Data Imputation

Camino, Ramiro Daniel; Hammerschmidt, Christian; State, Radu

Reference : Working with Deep Generative Models and Tabular Data Imputation


	Document type :	Scientific congresses, symposiums and conference proceedings : Unpublished conference
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Computational Sciences
	To cite this reference:	http://hdl.handle.net/10993/44101

Title :	Working with Deep Generative Models and Tabular Data Imputation
Language :	English
Author, co-author :	Camino, Ramiro Daniel [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Hammerschmidt, Christian [Delft University of Technology]
	State, Radu [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Publication date :	17-Jul-2020
Peer reviewed :	Yes
On invitation :	No
Audience :	International
Event name :	First Workshop on the Art of Learning with Missing Values (Artemiss)
Event date :	from 12-07-2020 to 18-07-2020
Event organizer :	Hosted by the 37th International Conference on Machine Learning (ICML)
Event place (city) :	Vienna
Event country :	Austria
Abstract :	[en] Datasets with missing values are very common in industry applications. Missing data typically have a negative impact on machine learning models. With the rise of generative models in deep learning, recent studies proposed solutions to the problem of imputing missing values based various deep generative models. Previous experiments with Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) showed promising results in this domain. Initially, these results focused on imputation in image data, e.g. filling missing patches in images. Recent proposals addressed missing values in tabular data. For these data, the case for deep generative models seems to be less clear. In the process of providing a fair comparison of proposed methods, we uncover several issues when assessing the status quo: the use of under-specified and ambiguous dataset names, the large range of parameters and hyper-parameters to tune for each method, and the use of different metrics and evaluation methods.
Permalink :	http://hdl.handle.net/10993/44101

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