"Mitigating Temporal-Drift: A Simple Approach to Keep NER Models Crisp" (SocialNLP at NAACL 2021)
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Updated
Jul 17, 2021 - Python
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temporal-drift
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Builds a fraud detection system on IEEE-CIS data that explicitly models temporal distribution shift by training adversarial validators to detect when the production distribution diverges from training data, then dynamically reweights ensemble members (LightGBM, CatBoost, XGBoost) based on their robustness to detected drift regimes.
python machine-learning xgboost lightgbm ensemble-learning fraud-detection catboost adversarial-validation temporal-drift
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Updated
Feb 21, 2026 - Python
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