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arXiv Statistics ML By Xiaobo Guo, Lu-an Dong, Yanbo Wang, Peng Zhang, Cai Zhi, Youru Li

DTD-VAE: Disentangled Temporal Dependencies VAE for Credit Risk Prediction

Read the original on arXiv Statistics ML →

The paper introduces DTD‑VAE, a Variational Autoencoder that disentangles temporal dependencies to better predict credit risk. It uses an autoregressive feature inference module to capture temporal patterns among latent variables and an element‑wise gating mechanism in the generative module to assign independent weights to each latent dimension, especially those relevant to credit risk. Experiments on six real‑world datasets show the model outperforms existing methods, improving ROC‑AUC by 3.2%–4.86% and Accuracy Ratio by 6.41%–9.71%.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Statistics ML.

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