Weakly Supervised Learners for Correction of AI Errors with Provable Performance Guarantees
Fuente:
arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866929241646432256 |
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| author | Tyukin, Ivan Y. Tyukina, Tatiana van Helden, Daniel Zheng, Zedong Mirkes, Evgeny M. Sutton, Oliver J. Zhou, Qinghua Gorban, Alexander N. Allison, Penelope |
| author_facet | Tyukin, Ivan Y. Tyukina, Tatiana van Helden, Daniel Zheng, Zedong Mirkes, Evgeny M. Sutton, Oliver J. Zhou, Qinghua Gorban, Alexander N. Allison, Penelope |
| contents | We present a new methodology for handling AI errors by introducing weakly supervised AI error correctors with a priori performance guarantees. These AI correctors are auxiliary maps whose role is to moderate the decisions of some previously constructed underlying classifier by either approving or rejecting its decisions. The rejection of a decision can be used as a signal to suggest abstaining from making a decision. A key technical focus of the work is in providing performance guarantees for these new AI correctors through bounds on the probabilities of incorrect decisions. These bounds are distribution agnostic and do not rely on assumptions on the data dimension. Our empirical example illustrates how the framework can be applied to improve the performance of an image classifier in a challenging real-world task where training data are scarce. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_00899 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Weakly Supervised Learners for Correction of AI Errors with Provable Performance Guarantees Tyukin, Ivan Y. Tyukina, Tatiana van Helden, Daniel Zheng, Zedong Mirkes, Evgeny M. Sutton, Oliver J. Zhou, Qinghua Gorban, Alexander N. Allison, Penelope Machine Learning Artificial Intelligence 68T05, 68T37 We present a new methodology for handling AI errors by introducing weakly supervised AI error correctors with a priori performance guarantees. These AI correctors are auxiliary maps whose role is to moderate the decisions of some previously constructed underlying classifier by either approving or rejecting its decisions. The rejection of a decision can be used as a signal to suggest abstaining from making a decision. A key technical focus of the work is in providing performance guarantees for these new AI correctors through bounds on the probabilities of incorrect decisions. These bounds are distribution agnostic and do not rely on assumptions on the data dimension. Our empirical example illustrates how the framework can be applied to improve the performance of an image classifier in a challenging real-world task where training data are scarce. |
| title | Weakly Supervised Learners for Correction of AI Errors with Provable Performance Guarantees |
| topic | Machine Learning Artificial Intelligence 68T05, 68T37 |
| url | https://arxiv.org/abs/2402.00899 |