Addressing Distribution Shift in RTB Markets via Exponential Tilting
This addresses performance drops due to distribution shifts in RTB markets, but it is incremental as it applies an existing method to a specific domain.
The study tackled distribution shift in Real-Time Bidding (RTB) markets by applying the Exponential Tilt Reweighting Alignment (ExTRA) algorithm to correct for selection bias in binary classification models, evaluating its efficiency on simulated real-world data.
In machine learning applications, distribution shifts between training and target environments can lead to significant drops in model performance. This study investigates the impact of such shifts on binary classification models within the Real-Time Bidding (RTB) market context, where selection bias contributes to these shifts. To address this challenge, we apply the Exponential Tilt Reweighting Alignment (ExTRA) algorithm, proposed by Maity et al. (2023). This algorithm estimates importance weights for the empirical risk by considering both covariate and label distributions, without requiring target label information, by assuming a specific weight structure. The goal of this study is to estimate weights that correct for the distribution shifts in RTB model and to evaluate the efficiency of the proposed model using simulated real-world data.