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

arXiv: https://arxiv.org/abs/2004.03045 · HTML: https://mlsanity.com/paper/2004.03045/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2020.

## Built on by (most cited first, 2 of 2)

- [SLM: End-to-end Feature Selection via Sparse Learnable Masks](https://mlsanity.com/paper/2304.03202/) (2023)
- [Acoustic UAV Detection in Battlefield Scenarios: Handling Noise, Domain Shift, and Weak Labels](https://mlsanity.com/paper/2608.14287/) (2026)

## Abstract (arXiv)

In user targeting automation systems, concept drift in input data is one of the main challenges. It deteriorates model performance on new data over time. Previous research on concept drift mostly proposed model retraining after observing performance decreases. However, this approach is suboptimal because the system fixes the problem only after suffering from poor performance on new data. Here, we introduce an adversarial validation approach to concept drift problems in user targeting automation systems. With our approach, the system detects concept drift in new data before making inference, trains a model, and produces predictions adapted to the new data. We show that our approach addresses concept drift effectively with the AutoML3 Lifelong Machine Learning challenge data as well as in Uber's internal user targeting automation system, MaLTA.