Addressing Distribution Shift in RTB Markets via Exponential Tilting
Minji Kim Seong Jin Lee Bumsik Kim
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.