Saved in:
| Main Authors: | , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.08445 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909953950744576 |
|---|---|
| author | Jiao, Yining Bhamidi, Sreekalyani Qu, Huaizhi Zdanski, Carlton Kimbell, Julia Prince, Andrew Worden, Cameron Kirse, Samuel Rutter, Christopher Shields, Benjamin Dunn, William Mahmud, Jisan Chen, Tianlong Niethammer, Marc |
| author_facet | Jiao, Yining Bhamidi, Sreekalyani Qu, Huaizhi Zdanski, Carlton Kimbell, Julia Prince, Andrew Worden, Cameron Kirse, Samuel Rutter, Christopher Shields, Benjamin Dunn, William Mahmud, Jisan Chen, Tianlong Niethammer, Marc |
| contents | The goal of this work is to develop principled techniques to extract information from high dimensional data sets with complex dependencies in areas such as medicine that can provide insight into individual as well as population level variation. We develop $\texttt{LucidAtlas}$, an approach that can represent spatially varying information, and can capture the influence of covariates as well as population uncertainty. As a versatile atlas representation, $\texttt{LucidAtlas}$ offers robust capabilities for covariate interpretation, individualized prediction, population trend analysis, and uncertainty estimation, with the flexibility to incorporate prior knowledge. Additionally, we discuss the trustworthiness and potential risks of neural additive models for analyzing dependent covariates and then introduce a marginalization approach to explain the dependence of an individual predictor on the models' response (the atlas). To validate our method, we demonstrate its generalizability on two medical datasets. Our findings underscore the critical role of by-construction interpretable models in advancing scientific discovery. Our code will be publicly available upon acceptance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_08445 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LucidAtlas: Learning Uncertainty-Aware, Covariate-Disentangled, Individualized Atlas Representations Jiao, Yining Bhamidi, Sreekalyani Qu, Huaizhi Zdanski, Carlton Kimbell, Julia Prince, Andrew Worden, Cameron Kirse, Samuel Rutter, Christopher Shields, Benjamin Dunn, William Mahmud, Jisan Chen, Tianlong Niethammer, Marc Machine Learning The goal of this work is to develop principled techniques to extract information from high dimensional data sets with complex dependencies in areas such as medicine that can provide insight into individual as well as population level variation. We develop $\texttt{LucidAtlas}$, an approach that can represent spatially varying information, and can capture the influence of covariates as well as population uncertainty. As a versatile atlas representation, $\texttt{LucidAtlas}$ offers robust capabilities for covariate interpretation, individualized prediction, population trend analysis, and uncertainty estimation, with the flexibility to incorporate prior knowledge. Additionally, we discuss the trustworthiness and potential risks of neural additive models for analyzing dependent covariates and then introduce a marginalization approach to explain the dependence of an individual predictor on the models' response (the atlas). To validate our method, we demonstrate its generalizability on two medical datasets. Our findings underscore the critical role of by-construction interpretable models in advancing scientific discovery. Our code will be publicly available upon acceptance. |
| title | LucidAtlas: Learning Uncertainty-Aware, Covariate-Disentangled, Individualized Atlas Representations |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2502.08445 |