Addressing Distribution Shift in RTB Markets via Exponential Tilting

• URL: http://arxiv.org/abs/2308.07424v1
• Date: Mon, 14 Aug 2023 19:31:58 GMT
• Title: Addressing Distribution Shift in RTB Markets via Exponential Tilting
• Authors: Minji Kim, Seong Jin Lee, Bumsik Kim
• Abstract summary: This paper introduces the Exponential Tilt Reweighting Alignment (ExTRA) algorithm to address distribution shifts in data. 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.
• Score: 2.883257292731477
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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