Practical Modelling of Mixed-Tailed Data with Normalizing Flows
| Authors | |
|---|---|
| Publication date | 2024 |
| Journal | Transactions on Machine Learning Research |
| Article number | 2998 |
| Volume | Issue number | 2024 | 10 |
| Number of pages | 19 |
| Organisations |
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| Abstract | Capturing the correct tail behavior is difficult, yet essential for a faithful generative model. In this work, we provide an improved framework for training flows-based models with robust capabilities to capture the tail behavior of mixed-tail data. We propose a combination of a tail-flexible base distribution and a robust training algorithm to enable the flow to model heterogeneous tail behavior in the target distribution. We support our claim with extensive experiments on synthetic and real world data. |
| Document type | Article |
| Language | English |
| Published at |
https://openreview.net/forum?id=uphsKDj0Uu
(Final published version)
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| Other links | |
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Practical Modelling of Mixed-Tailed Data with Normalizing Flows
(Final published version)
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