# Feature Shift Localization Network

arXiv: https://arxiv.org/abs/2506.09101 · HTML: https://mlsanity.com/paper/2506.09101/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 74 references that are in the map.
- First posted on arXiv 10 Jun 2025.

## Builds on (most cited first, 22 of 22)

- [E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials](https://mlsanity.com/paper/2101.03164/) (2021)
- [OpenML: networked science in machine learning](https://mlsanity.com/paper/1407.7722/) (2014)
- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Optimal Transport for Domain Adaptation](https://mlsanity.com/paper/1507.00504/) (2015)
- [Generative Moment Matching Networks](https://mlsanity.com/paper/1502.02761/) (2015)
- [Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift](https://mlsanity.com/paper/1810.11953/) (2018)
- [Data-centric Artificial Intelligence: A Survey](https://mlsanity.com/paper/2303.10158/) (2023)
- [Optimal Transport Aggregation for Visual Place Recognition](https://mlsanity.com/paper/2311.15937/) (2023)
- [Recent Advances in Optimal Transport for Machine Learning](https://mlsanity.com/paper/2306.16156/) (2023)
- [Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling](https://mlsanity.com/paper/2012.06046/) (2020)
- [On the Convergence and Robustness of Training GANs with Regularized Optimal Transport](https://mlsanity.com/paper/1802.08249/) (2018)
- [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)
- [Efficient Attention via Control Variates](https://mlsanity.com/paper/2302.04542/) (2023)
- [What is an equivariant neural network?](https://mlsanity.com/paper/2205.07362/) (2022)
- [Multi-head or Single-head? An Empirical Comparison for Transformer Training](https://mlsanity.com/paper/2106.09650/) (2021)
- [Request-and-Reverify: Hierarchical Hypothesis Testing for Concept Drift Detection with Expensive Labels](https://mlsanity.com/paper/1806.10131/) (2018)
- [Approximating optimal transport with linear programs](https://mlsanity.com/paper/1810.05957/) (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)
- [Filter Methods for Feature Selection in Supervised Machine Learning Applications -- Review and Benchmark](https://mlsanity.com/paper/2111.12140/) (2021)
- [Adversarial Learning for Feature Shift Detection and Correction](https://mlsanity.com/paper/2312.04546/) (2023)

## Abstract (arXiv)

Feature shifts between data sources are present in many applications involving healthcare, biomedical, socioeconomic, financial, survey, and multi-sensor data, among others, where unharmonized heterogeneous data sources, noisy data measurements, or inconsistent processing and standardization pipelines can lead to erroneous features. Localizing shifted features is important to address the underlying cause of the shift and correct or filter the data to avoid degrading downstream analysis. While many techniques can detect distribution shifts, localizing the features originating them is still challenging, with current solutions being either inaccurate or not scalable to large and high-dimensional datasets. In this work, we introduce the Feature Shift Localization Network (FSL-Net), a neural network that can localize feature shifts in large and high-dimensional datasets in a fast and accurate manner. The network, trained with a large number of datasets, learns to extract the statistical properties of the datasets and can localize feature shifts from previously unseen datasets and shifts without the need for re-training. The code and ready-to-use trained model are available at https://github.com/AI-sandbox/FSL-Net.