Improving Predictive Inference under Covariate Shift by Weighting the Log-likelihood Function.¶
Shimodaira, H. (2000). Improving Predictive Inference under Covariate Shift by Weighting the Log-likelihood Function. Journal of Statistical Planning and Inference, 90(2), 227-244.
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Primes¶
- Training Serving Skew
- A model learns a predictor $\hat{f}$ by minimising loss over a preparation environment whose inputs are drawn from distribution $P_{\text{train}}(x)$, while the conditional relationship $P(y \mid x)$ is assumed stable.
This sourceFormalizes covariate shift — input distribution differs between training and deployment while P(y|x) is stable.
- A model learns a predictor $\hat{f}$ by minimising loss over a preparation environment whose inputs are drawn from distribution $P_{\text{train}}(x)$, while the conditional relationship $P(y \mid x)$ is assumed stable.
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