Distribution shift in machine learning models can be a primary cause of performance degradation. This paper delves into the characteristics of these shifts, primarily motivated by Real-Time Bidding (RTB) market models. We emphasize the challenges posed by class imbalance and sample selection bias, both potent instigators of distribution shifts. This paper introduces the Exponential Tilt Reweighting Alignment (ExTRA) algorithm, as proposed by Marty et al. (2023), to address distribution shifts in data. The ExTRA method is designed to determine the importance weights on the source data, aiming to minimize the KL divergence between the weighted source and target datasets. A notable advantage of this method is its ability to operate using labeled source data and unlabeled target data. Through simulated real-world data, we investigate the nature of distribution shift and evaluate the applicacy of the proposed model.

机器学习模型中的分布偏移会导致性能下降。本文研究了这些偏移的特征，主要关注实时竞价市场模型所带来的挑战，包括类别不平衡和样本选择偏差。本文提出了ExTRA算法，旨在通过确定源数据的重要性权重来最小化加权源数据和目标数据之间的KL散度。该方法具有使用标记的源数据和未标记的目标数据的能力。通过模拟真实世界的数据，我们研究了分布偏移的性质，并评估了所提出模型的适用性。

通过指数倾斜解决实时竞价市场中的分布偏移