109 research outputs found
Distributional Learning of Variational AutoEncoder: Application to Synthetic Data Generation
The Gaussianity assumption has been consistently criticized as a main
limitation of the Variational Autoencoder (VAE) despite its efficiency in
computational modeling. In this paper, we propose a new approach that expands
the model capacity (i.e., expressive power of distributional family) without
sacrificing the computational advantages of the VAE framework. Our VAE model's
decoder is composed of an infinite mixture of asymmetric Laplace distribution,
which possesses general distribution fitting capabilities for continuous
variables. Our model is represented by a special form of a nonparametric
M-estimator for estimating general quantile functions, and we theoretically
establish the relevance between the proposed model and quantile estimation. We
apply the proposed model to synthetic data generation, and particularly, our
model demonstrates superiority in easily adjusting the level of data privacy
Beta quantile regression for robust estimation of uncertainty in the presence of outliers
Quantile Regression (QR) can be used to estimate aleatoric uncertainty in
deep neural networks and can generate prediction intervals. Quantifying
uncertainty is particularly important in critical applications such as clinical
diagnosis, where a realistic assessment of uncertainty is essential in
determining disease status and planning the appropriate treatment. The most
common application of quantile regression models is in cases where the
parametric likelihood cannot be specified. Although quantile regression is
quite robust to outlier response observations, it can be sensitive to outlier
covariate observations (features). Outlier features can compromise the
performance of deep learning regression problems such as style translation,
image reconstruction, and deep anomaly detection, potentially leading to
misleading conclusions. To address this problem, we propose a robust solution
for quantile regression that incorporates concepts from robust divergence. We
compare the performance of our proposed method with (i) least trimmed quantile
regression and (ii) robust regression based on the regularization of
case-specific parameters in a simple real dataset in the presence of outlier.
These methods have not been applied in a deep learning framework. We also
demonstrate the applicability of the proposed method by applying it to a
medical imaging translation task using diffusion models
a literature review
Fonseca, J., & Bacao, F. (2023). Tabular and latent space synthetic data generation: a literature review. Journal of Big Data, 10, 1-37. [115]. https://doi.org/10.1186/s40537-023-00792-7 --- This research was supported by two research grants of the Portuguese Foundation for Science and Technology (“Fundação para a Ciência e a Tecnologia”), references SFRH/BD/151473/2021 and DSAIPA/DS/0116/2019, and by project UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC).The generation of synthetic data can be used for anonymization, regularization, oversampling, semi-supervised learning, self-supervised learning, and several other tasks. Such broad potential motivated the development of new algorithms, specialized in data generation for specific data formats and Machine Learning (ML) tasks. However, one of the most common data formats used in industrial applications, tabular data, is generally overlooked; Literature analyses are scarce, state-of-the-art methods are spread across domains or ML tasks and there is little to no distinction among the main types of mechanism underlying synthetic data generation algorithms. In this paper, we analyze tabular and latent space synthetic data generation algorithms. Specifically, we propose a unified taxonomy as an extension and generalization of previous taxonomies, review 70 generation algorithms across six ML problems, distinguish the main generation mechanisms identified into six categories, describe each type of generation mechanism, discuss metrics to evaluate the quality of synthetic data and provide recommendations for future research. We expect this study to assist researchers and practitioners identify relevant gaps in the literature and design better and more informed practices with synthetic data.publishersversionpublishe
CVAE: Gaussian Copula-based VAE Differing Disentangled from Coupled Representations with Contrastive Posterior
We present a self-supervised variational autoencoder (VAE) to jointly learn
disentangled and dependent hidden factors and then enhance disentangled
representation learning by a self-supervised classifier to eliminate coupled
representations in a contrastive manner. To this end, a Contrastive Copula VAE
(CVAE) is introduced without relying on prior knowledge about data in the
probabilistic principle and involving strong modeling assumptions on the
posterior in the neural architecture. CVAE simultaneously factorizes the
posterior (evidence lower bound, ELBO) with total correlation (TC)-driven
decomposition for learning factorized disentangled representations and extracts
the dependencies between hidden features by a neural Gaussian copula for copula
coupled representations. Then, a self-supervised contrastive classifier
differentiates the disentangled representations from the coupled
representations, where a contrastive loss regularizes this contrastive
classification together with the TC loss for eliminating entangled factors and
strengthening disentangled representations. CVAE demonstrates a strong
effect in enhancing disentangled representation learning. CVAE further
contributes to improved optimization addressing the TC-based VAE instability
and the trade-off between reconstruction and representation
Causally Disentangled Generative Variational AutoEncoder
We present a new supervised learning technique for the Variational
AutoEncoder (VAE) that allows it to learn a causally disentangled
representation and generate causally disentangled outcomes simultaneously. We
call this approach Causally Disentangled Generation (CDG). CDG is a generative
model that accurately decodes an output based on a causally disentangled
representation. Our research demonstrates that adding supervised regularization
to the encoder alone is insufficient for achieving a generative model with CDG,
even for a simple task. Therefore, we explore the necessary and sufficient
conditions for achieving CDG within a specific model. Additionally, we
introduce a universal metric for evaluating the causal disentanglement of a
generative model. Empirical results from both image and tabular datasets
support our findings
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