arXiv:2603.01800v2 Announce Type: replace Abstract: Heavy-tailed distributions are ubiquitous in real-world data, where rare but extreme events dominate risk and variability. However, standard Variational Autoencoders (VAEs) employ simple decoder distributions, such as Gaussian distributions, that fail to capture heavy-tailed behavior, while existing heavy-tail-aware extensions remain restricted to predefined parametric families whose tail behavior is fixed a priori. We propose the Phase-Type Variational Autoencoder (PH-VAE), whose decoder distribution is a latent-conditioned Phase-Type (PH) d
The continuous evolution of AI models demands more sophisticated statistical methods to handle complex, real-world data, especially heavy-tailed distributions which are prevalent in financial, environmental, and risk management applications.
Improving VAEs to properly model heavy-tailed data allows for more accurate and robust AI systems, crucial for applications where extreme events significantly impact outcomes and standard models fail.
Standard VAEs often assume Gaussian distributions, limiting their efficacy for data with rare but impactful events, while PH-VAEs offer a parametrically richer and more flexible approach to capture these characteristics.
- · AI researchers and developers
- · Financial modeling and risk management sectors
- · Insurance industry
- · Any sector dealing with extreme event prediction
- · Organizations relying solely on standard VAEs for heavy-tailed data
- · Simplified parametric modeling approaches
The PH-VAE provides a more accurate tool for generative modeling of datasets characterized by extreme values, improving data synthesis and anomaly detection.
Enhanced generative models could lead to better simulations for complex systems, informing decision-making in finance, climate science, and engineering.
More robust AI systems, capable of understanding and predicting extreme events, could mitigate risks and create new opportunities in previously unstable or unpredictable domains.
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Read at arXiv cs.LG