Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.18332 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918025397010432 |
|---|---|
| author | Maneriker, Pranav Vadlamani, Aditya T. Srinivasan, Anutam He, Yuntian Payani, Ali Parthasarathy, Srinivasan |
| author_facet | Maneriker, Pranav Vadlamani, Aditya T. Srinivasan, Anutam He, Yuntian Payani, Ali Parthasarathy, Srinivasan |
| contents | Conformal prediction has become increasingly popular for quantifying the uncertainty associated with machine learning models. Recent work in graph uncertainty quantification has built upon this approach for conformal graph prediction. The nascent nature of these explorations has led to conflicting choices for implementations, baselines, and method evaluation. In this work, we analyze the design choices made in the literature and discuss the tradeoffs associated with existing methods. Building on the existing implementations, we introduce techniques to scale existing methods to large-scale graph datasets without sacrificing performance. Our theoretical and empirical results justify our recommendations for future scholarship in graph conformal prediction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_18332 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Conformal Prediction: A Theoretical Note and Benchmarking Transductive Node Classification in Graphs Maneriker, Pranav Vadlamani, Aditya T. Srinivasan, Anutam He, Yuntian Payani, Ali Parthasarathy, Srinivasan Machine Learning Conformal prediction has become increasingly popular for quantifying the uncertainty associated with machine learning models. Recent work in graph uncertainty quantification has built upon this approach for conformal graph prediction. The nascent nature of these explorations has led to conflicting choices for implementations, baselines, and method evaluation. In this work, we analyze the design choices made in the literature and discuss the tradeoffs associated with existing methods. Building on the existing implementations, we introduce techniques to scale existing methods to large-scale graph datasets without sacrificing performance. Our theoretical and empirical results justify our recommendations for future scholarship in graph conformal prediction. |
| title | Conformal Prediction: A Theoretical Note and Benchmarking Transductive Node Classification in Graphs |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2409.18332 |