Addressing Distribution Shift in RTB Markets via Exponential Tilting
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Abstract (arXiv)
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.
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References · 8
- 2014Explaining and Harnessing Adversarial Examplescited by 7,364
- 2019Benchmarking Neural Network Robustness to Common Corruptions and Perturbationscited by 2,877
- 2016Deep Exploration via Bootstrapped DQNcited by 527
- 2019Conformal Prediction Under Covariate Shiftcited by 519
- 2021Dive into Deep Learningcited by 111
- 2020Tilted Empirical Risk Minimizationcited by 106
- 2020Minimax optimal approaches to the label shift problem in non-parametric settingscited by 20
- 2022Understanding new tasks through the lens of training data via exponential tiltingcited by 11
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