Causal Covariate Shift Correction using Fisher information penalty
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912241416142848 |
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| author | Khan, Behraj Mirza, Behroz Syed, Tahir |
| author_facet | Khan, Behraj Mirza, Behroz Syed, Tahir |
| contents | Evolving feature densities across batches of training data bias cross-validation, making model selection and assessment unreliable (\cite{sugiyama2012machine}). This work takes a distributed density estimation angle to the training setting where data are temporally distributed. \textit{Causal Covariate Shift Correction ($C^{3}$)}, accumulates knowledge about the data density of a training batch using Fisher Information, and using it to penalize the loss in all subsequent batches. The penalty improves accuracy by $12.9\%$ over the full-dataset baseline, by $20.3\%$ accuracy at maximum in batchwise and $5.9\%$ at minimum in foldwise benchmarks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_15756 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Causal Covariate Shift Correction using Fisher information penalty Khan, Behraj Mirza, Behroz Syed, Tahir Machine Learning Evolving feature densities across batches of training data bias cross-validation, making model selection and assessment unreliable (\cite{sugiyama2012machine}). This work takes a distributed density estimation angle to the training setting where data are temporally distributed. \textit{Causal Covariate Shift Correction ($C^{3}$)}, accumulates knowledge about the data density of a training batch using Fisher Information, and using it to penalize the loss in all subsequent batches. The penalty improves accuracy by $12.9\%$ over the full-dataset baseline, by $20.3\%$ accuracy at maximum in batchwise and $5.9\%$ at minimum in foldwise benchmarks. |
| title | Causal Covariate Shift Correction using Fisher information penalty |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2502.15756 |