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On efficiency gains via augmenting a tiny sample with a massive auxiliary sample
In this paper, we study the problem of augmenting a tiny target sample with a massive auxiliary sample. Utilizing Tukey's factorization, there are two popular approaches: the inverse probability weight (IPW) and the full-likelihood (FL) methods. We show that the IPW approach suffers from the limited target sample problem while the FL method may estimate some model parameters at the rate of the massive auxiliary sample size, a phenomenon we call full efficiency gain. We study the theory behind the full efficiency gain for exponential families and mixtures of exponential families. We also study
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-27T04:46:30.000Z
First collected: 2026-09-21T08:51:59.673Z. This is not the publication date.