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.

翻译：尽管现代训练数据集规模庞大，但将机器学习模型部署到新任务仍是一项重大挑战。然而，通过对训练数据进行重新加权，使其更能代表新（目标）任务，这或许可行。本文研究了如何对训练样本进行重新加权，以洞悉目标任务的数据分布。具体而言，我们基于指数倾斜假设建立了一个分布偏移模型，并通过最小化有标签训练数据集与无标签目标数据集之间的KL散度，学习训练数据的重要性权重。学得的训练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。我们在Waterbirds和Breeds基准数据集上验证了该方法的有效性。