# Sliced-Wasserstein-based Anomaly Detection and Open Dataset for Localized Critical Peak Rebates

arXiv: https://arxiv.org/abs/2410.21712 · HTML: https://mlsanity.com/paper/2410.21712/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 33 references that are in the map.
- First posted on arXiv 29 Oct 2024.

## Builds on (most cited first, 11 of 11)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Bayesian Deep Learning and a Probabilistic Perspective of Generalization](https://mlsanity.com/paper/2002.08791/) (2020)
- [Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Users](https://mlsanity.com/paper/2007.06823/) (2020)
- [Generalized Sliced Wasserstein Distances](https://mlsanity.com/paper/1902.00434/) (2019)
- [Physics-Informed Neural Networks for Power Systems](https://mlsanity.com/paper/1911.03737/) (2019)
- [Distributionally Robust Learning](https://mlsanity.com/paper/2108.08993/) (2021)
- [Verification of Neural Network Behaviour: Formal Guarantees for Power System Applications](https://mlsanity.com/paper/1910.01624/) (2019)
- [WOOD: Wasserstein-based Out-of-Distribution Detection](https://mlsanity.com/paper/2112.06384/) (2021)
- [Closing the Loop: A Framework for Trustworthy Machine Learning in Power Systems](https://mlsanity.com/paper/2203.07505/) (2022)
- [Wasserstein Distributionally Robust Shallow Convex Neural Networks](https://mlsanity.com/paper/2407.16800/) (2024)

## Built on by (most cited first, 1 of 1)

- [Sliced-Wasserstein Distance-based Data Selection](https://mlsanity.com/paper/2504.12918/) (2025)

## Abstract (arXiv)

In this work, we present a new unsupervised anomaly (outlier) detection (AD) method using the sliced-Wasserstein metric. This filtering technique is conceptually interesting for MLOps pipelines deploying machine learning models in critical sectors, e.g., energy, as it offers a conservative data selection. Additionally, we open the first dataset showcasing localized critical peak rebate demand response in a northern climate. We demonstrate the capabilities of our method on synthetic datasets as well as standard AD datasets and use it in the making of a first benchmark for our open-source localized critical peak rebate dataset.