How Inverse Conditional Flows Can Serve as a Substitute for Distributional Regression
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914863992799232 |
|---|---|
| author | Kook, Lucas Kolb, Chris Schiele, Philipp Dold, Daniel Arpogaus, Marcel Fritz, Cornelius Baumann, Philipp F. Kopper, Philipp Pielok, Tobias Dorigatti, Emilio Rügamer, David |
| author_facet | Kook, Lucas Kolb, Chris Schiele, Philipp Dold, Daniel Arpogaus, Marcel Fritz, Cornelius Baumann, Philipp F. Kopper, Philipp Pielok, Tobias Dorigatti, Emilio Rügamer, David |
| contents | Neural network representations of simple models, such as linear regression, are being studied increasingly to better understand the underlying principles of deep learning algorithms. However, neural representations of distributional regression models, such as the Cox model, have received little attention so far. We close this gap by proposing a framework for distributional regression using inverse flow transformations (DRIFT), which includes neural representations of the aforementioned models. We empirically demonstrate that the neural representations of models in DRIFT can serve as a substitute for their classical statistical counterparts in several applications involving continuous, ordered, time-series, and survival outcomes. We confirm that models in DRIFT empirically match the performance of several statistical methods in terms of estimation of partial effects, prediction, and aleatoric uncertainty quantification. DRIFT covers both interpretable statistical models and flexible neural networks opening up new avenues in both statistical modeling and deep learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_05429 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | How Inverse Conditional Flows Can Serve as a Substitute for Distributional Regression Kook, Lucas Kolb, Chris Schiele, Philipp Dold, Daniel Arpogaus, Marcel Fritz, Cornelius Baumann, Philipp F. Kopper, Philipp Pielok, Tobias Dorigatti, Emilio Rügamer, David Machine Learning Artificial Intelligence Computation Neural network representations of simple models, such as linear regression, are being studied increasingly to better understand the underlying principles of deep learning algorithms. However, neural representations of distributional regression models, such as the Cox model, have received little attention so far. We close this gap by proposing a framework for distributional regression using inverse flow transformations (DRIFT), which includes neural representations of the aforementioned models. We empirically demonstrate that the neural representations of models in DRIFT can serve as a substitute for their classical statistical counterparts in several applications involving continuous, ordered, time-series, and survival outcomes. We confirm that models in DRIFT empirically match the performance of several statistical methods in terms of estimation of partial effects, prediction, and aleatoric uncertainty quantification. DRIFT covers both interpretable statistical models and flexible neural networks opening up new avenues in both statistical modeling and deep learning. |
| title | How Inverse Conditional Flows Can Serve as a Substitute for Distributional Regression |
| topic | Machine Learning Artificial Intelligence Computation |
| url | https://arxiv.org/abs/2405.05429 |