# Adversarial Learning for Feature Shift Detection and Correction

arXiv: https://arxiv.org/abs/2312.04546 · HTML: https://mlsanity.com/paper/2312.04546/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 103 references that are in the map.
- First posted on arXiv 7 Dec 2023.

## Builds on (most cited first, 30 of 30)

- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models](https://mlsanity.com/paper/1708.03999/) (2017)
- [OpenML: networked science in machine learning](https://mlsanity.com/paper/1407.7722/) (2014)
- [f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization](https://mlsanity.com/paper/1606.00709/) (2016)
- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [GAIN: Missing Data Imputation using Generative Adversarial Nets](https://mlsanity.com/paper/1806.02920/) (2018)
- [Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift](https://mlsanity.com/paper/1810.11953/) (2018)
- [CatBoost: gradient boosting with categorical features support](https://mlsanity.com/paper/1810.11363/) (2018)
- [Data-centric Artificial Intelligence: A Survey](https://mlsanity.com/paper/2303.10158/) (2023)
- [Generalizing to unseen domains via distribution matching](https://mlsanity.com/paper/1911.00804/) (2019)
- [Matrix Completion and Low-Rank SVD via Fast Alternating Least Squares](https://mlsanity.com/paper/1410.2596/) (2014)
- [Missing Data Imputation using Optimal Transport](https://mlsanity.com/paper/2002.03860/) (2020)
- [HyperImpute: Generalized Iterative Imputation with Automatic Model Selection](https://mlsanity.com/paper/2206.07769/) (2022)
- [MisGAN: Learning from Incomplete Data with Generative Adversarial Networks](https://mlsanity.com/paper/1902.09599/) (2019)
- [Deep Weakly-supervised Anomaly Detection](https://mlsanity.com/paper/1910.13601/) (2019)
- [Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling](https://mlsanity.com/paper/2012.06046/) (2020)
- [MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms](https://mlsanity.com/paper/2111.03187/) (2021)
- [Dataset Discovery in Data Lakes](https://mlsanity.com/paper/2011.10427/) (2020)
- [Feature Shift Detection: Localizing Which Features Have Shifted via Conditional Distribution Tests](https://mlsanity.com/paper/2107.06929/) (2021)
- [AlphaClean: Automatic Generation of Data Cleaning Pipelines](https://mlsanity.com/paper/1904.11827/) (2019)
- [PC-GAIN: Pseudo-label Conditional Generative Adversarial Imputation Networks for Incomplete Data](https://mlsanity.com/paper/2011.07770/) (2020)
- [Multiple Imputation via Generative Adversarial Network for High-dimensional Blockwise Missing Value Problems](https://mlsanity.com/paper/2112.11507/) (2021)
- [MLDemon: Deployment Monitoring for Machine Learning Systems](https://mlsanity.com/paper/2104.13621/) (2021)
- [Request-and-Reverify: Hierarchical Hypothesis Testing for Concept Drift Detection with Expensive Labels](https://mlsanity.com/paper/1806.10131/) (2018)
- [Online Decorrelation of Humidity and Temperature in Chemical Sensors for Continuous Monitoring](https://mlsanity.com/paper/1608.01719/) (2016)
- [Diverse Instances-Weighting Ensemble based on Region Drift Disagreement for Concept Drift Adaptation](https://mlsanity.com/paper/2004.05810/) (2020)
- [Iterative Alignment Flows](https://mlsanity.com/paper/2104.07232/) (2021)
- [Filter Methods for Feature Selection in Supervised Machine Learning Applications -- Review and Benchmark](https://mlsanity.com/paper/2111.12140/) (2021)
- [Parametric Adversarial Divergences are Good Losses for Generative Modeling](https://mlsanity.com/paper/1708.02511/) (2017)
- [Convergence Rates for Empirical Estimation of Binary Classification Bounds](https://mlsanity.com/paper/1810.01015/) (2018)

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

- [Keeping Medical AI Healthy and Trustworthy: A Review of Detection and Correction Methods for System Degradation](https://mlsanity.com/paper/2506.17442/) (2025)
- [Feature Shift Localization Network](https://mlsanity.com/paper/2506.09101/) (2025)

## Abstract (arXiv)

Data shift is a phenomenon present in many real-world applications, and while there are multiple methods attempting to detect shifts, the task of localizing and correcting the features originating such shifts has not been studied in depth. Feature shifts can occur in many datasets, including in multi-sensor data, where some sensors are malfunctioning, or in tabular and structured data, including biomedical, financial, and survey data, where faulty standardization and data processing pipelines can lead to erroneous features. In this work, we explore using the principles of adversarial learning, where the information from several discriminators trained to distinguish between two distributions is used to both detect the corrupted features and fix them in order to remove the distribution shift between datasets. We show that mainstream supervised classifiers, such as random forest or gradient boosting trees, combined with simple iterative heuristics, can localize and correct feature shifts, outperforming current statistical and neural network-based techniques. The code is available at https://github.com/AI-sandbox/DataFix.