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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2310.17159 |
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| _version_ | 1866929269349810176 |
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| author | Neo, Dexter Winkler, Stefan Chen, Tsuhan |
| author_facet | Neo, Dexter Winkler, Stefan Chen, Tsuhan |
| contents | We present a new loss function that addresses the out-of-distribution (OOD) calibration problem. While many objective functions have been proposed to effectively calibrate models in-distribution, our findings show that they do not always fare well OOD. Based on the Principle of Maximum Entropy, we incorporate helpful statistical constraints observed during training, delivering better model calibration without sacrificing accuracy. We provide theoretical analysis and show empirically that our method works well in practice, achieving state-of-the-art calibration on both synthetic and real-world benchmarks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_17159 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | MaxEnt Loss: Constrained Maximum Entropy for Calibration under Out-of-Distribution Shift Neo, Dexter Winkler, Stefan Chen, Tsuhan Machine Learning We present a new loss function that addresses the out-of-distribution (OOD) calibration problem. While many objective functions have been proposed to effectively calibrate models in-distribution, our findings show that they do not always fare well OOD. Based on the Principle of Maximum Entropy, we incorporate helpful statistical constraints observed during training, delivering better model calibration without sacrificing accuracy. We provide theoretical analysis and show empirically that our method works well in practice, achieving state-of-the-art calibration on both synthetic and real-world benchmarks. |
| title | MaxEnt Loss: Constrained Maximum Entropy for Calibration under Out-of-Distribution Shift |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2310.17159 |