论文标题
通过指数倾斜来了解培训数据镜头的新任务
Understanding new tasks through the lens of training data via exponential tilting
论文作者
论文摘要
尽管现代培训数据集的规模很大，但将机器学习模型部署到新任务是一个重大挑战。但是，可以想象可以将培训数据重新加权以更代表新的（目标）任务。我们考虑重新培训样本以了解目标任务分布的问题。具体而言，我们根据指数倾斜的假设制定了分配移位模型，并学习火车数据重要性权重，最大程度地减少了标记的火车和未标记的目标数据集之间的KL差异。然后，学习的火车数据权重可用于下游任务，例如目标性能评估，微调和模型选择。我们证明了我们方法对水鸟的功效，并繁殖了基准。
Deploying machine learning models to new tasks is a major challenge despite the large size of the modern training datasets. However, it is conceivable that the training data can be reweighted to be more representative of the new (target) task. We consider the problem of reweighing the training samples to gain insights into the distribution of the target task. Specifically, we formulate a distribution shift model based on the exponential tilt assumption and learn train data importance weights minimizing the KL divergence between labeled train and unlabeled target datasets. The learned train data weights can then be used for downstream tasks such as target performance evaluation, fine-tuning, and model selection. We demonstrate the efficacy of our method on Waterbirds and Breeds benchmarks.
