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In user targeting automation systems, concept drift in input data is one ofthe main challenges. It deteriorates model performance on new data over time.Previous research on concept drift mostly proposed model retraining afterobserving performance decreases. However, this approach is suboptimal becausethe system fixes the problem only after suffering from poor performance on newdata. Here, we introduce an adversarial validation approach to concept driftproblems in user targeting automation systems. With our approach, the systemdetects concept drift in new data before making inference, trains a model, andproduces predictions adapted to the new data. We show that our approachaddresses concept drift effectively with the AutoML3 Lifelong Machine Learningchallenge data as well as in Uber's internal user targeting automation system,MaLTA.

Adversarial Validation Approach to Concept Drift Problem in User Targeting Automation Systems at Uber