Problem
Research questions and friction points this paper is trying to address.
tiny sample
massive auxiliary sample
efficiency gain
exponential families
inverse probability weight
On efficiency gains via augmenting a tiny sample with a massive auxiliary sample
🤖 AI Summary
本文研究了利用大量辅助样本增强小目标样本的问题，对比了IPW和FL方法，发现FL方法在某些情况下可实现全效率增益。
📝 Abstract
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 the efficiency gain for the IPW method under a nonparametric procedure and show how it can achieve a parametric rate of the target sample size. As a side note, we also discuss how one may use FL to train neural network models simultaneously for both the target distribution and the odds model.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Full Efficiency Gain
Inverse Probability Weight
Exponential Families
Neural Network Models
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