C-Adapter: Adapting Deep Classifiers for Efficient Conformal Prediction Sets
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909540203626496 |
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| author | Liu, Kangdao Zeng, Hao Huang, Jianguo Zhuang, Huiping Vong, Chi-Man Wei, Hongxin |
| author_facet | Liu, Kangdao Zeng, Hao Huang, Jianguo Zhuang, Huiping Vong, Chi-Man Wei, Hongxin |
| contents | Conformal prediction, as an emerging uncertainty quantification technique, typically functions as post-hoc processing for the outputs of trained classifiers. To optimize the classifier for maximum predictive efficiency, Conformal Training rectifies the training objective with a regularization that minimizes the average prediction set size at a specific error rate. However, the regularization term inevitably deteriorates the classification accuracy and leads to suboptimal efficiency of conformal predictors. To address this issue, we introduce \textbf{Conformal Adapter} (C-Adapter), an adapter-based tuning method to enhance the efficiency of conformal predictors without sacrificing accuracy. In particular, we implement the adapter as a class of intra order-preserving functions and tune it with our proposed loss that maximizes the discriminability of non-conformity scores between correctly and randomly matched data-label pairs. Using C-Adapter, the model tends to produce extremely high non-conformity scores for incorrect labels, thereby enhancing the efficiency of prediction sets across different coverage rates. Extensive experiments demonstrate that C-Adapter can effectively adapt various classifiers for efficient prediction sets, as well as enhance the conformal training method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_09408 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | C-Adapter: Adapting Deep Classifiers for Efficient Conformal Prediction Sets Liu, Kangdao Zeng, Hao Huang, Jianguo Zhuang, Huiping Vong, Chi-Man Wei, Hongxin Machine Learning Conformal prediction, as an emerging uncertainty quantification technique, typically functions as post-hoc processing for the outputs of trained classifiers. To optimize the classifier for maximum predictive efficiency, Conformal Training rectifies the training objective with a regularization that minimizes the average prediction set size at a specific error rate. However, the regularization term inevitably deteriorates the classification accuracy and leads to suboptimal efficiency of conformal predictors. To address this issue, we introduce \textbf{Conformal Adapter} (C-Adapter), an adapter-based tuning method to enhance the efficiency of conformal predictors without sacrificing accuracy. In particular, we implement the adapter as a class of intra order-preserving functions and tune it with our proposed loss that maximizes the discriminability of non-conformity scores between correctly and randomly matched data-label pairs. Using C-Adapter, the model tends to produce extremely high non-conformity scores for incorrect labels, thereby enhancing the efficiency of prediction sets across different coverage rates. Extensive experiments demonstrate that C-Adapter can effectively adapt various classifiers for efficient prediction sets, as well as enhance the conformal training method. |
| title | C-Adapter: Adapting Deep Classifiers for Efficient Conformal Prediction Sets |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2410.09408 |