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BAFF: Bid-Aware Filter Family for Mitigating Training Data Interference in RTB A/B Tests
In online A/B tests for real-time bidding (RTB), control and treatment models are typically trained on a shared serving log that includes data generated by the counterpart model. This shared-log training biases each model's training data through two channels: the counterpart model may have selected
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