Summary of Stabilizing the Kumaraswamy Distribution, by Max Wasserman et al.
Stabilizing the Kumaraswamy Distribution
by Max Wasserman, Gonzalo Mateos
First submitted to arxiv on: 1 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a solution to improve large-scale latent variable models by introducing a new continuous distribution called Kumaraswamy (KS) that supports efficient sampling and low-variance differentiation through the reparameterization trick. The authors identify and resolve numerical instabilities in the inverse CDF and log-pdf of the KS distribution, which has been previously overlooked in libraries like PyTorch and TensorFlow. They then develop simple and scalable latent variable models based on the stabilized KS distribution, demonstrating its potential to enhance exploration-exploitation trade-offs in contextual multi-armed bandits and uncertainty quantification for link prediction with graph neural networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding a way to make big models that use continuous distributions work better. It’s about solving some math problems so these models can be used more efficiently. The authors show how they fixed some issues with a specific distribution called Kumaraswamy, which could help make these big models work better for things like choosing the best option in different situations or predicting what links exist between things. |
