Stabilizing the Kumaraswamy Distribution
<|reference_start|>Stabilizing the Kumaraswamy Distribution: Large-scale latent variable models require expressive continuous distributions that support efficient sampling and low-variance differentiation, achievable through the reparameterization trick. The Kumaraswamy (KS) distribution is both expressive and supports the reparameterization trick with a simple closed-form inverse CDF. Yet, its adoption remains limited. We identify and resolve numerical instabilities in the inverse CDF and log-pdf, exposing issues in libraries like PyTorch and TensorFlow. We then introduce simple and scalable latent variable models based on the KS, improving exploration-exploitation trade-offs in contextual multi-armed bandits and enhancing uncertainty quantification for link prediction with graph neural networks. Our results support the stabilized KS distribution as a core component in scalable variational models for bounded latent variables.<|reference_end|>
@article{wasserman2024stabilizing,
    title={Stabilizing the Kumaraswamy Distribution},
    author={Max Wasserman, Gonzalo Mateos},
    journal={arXiv preprint arXiv:2410.00660},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.00660},
    primaryClass={cs.LG stat.ML}
}