Sliced Wasserstein Distance

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High-Dimensional Online Change Point Detection with Adaptive Thresholding and Interpretability
Sep 21, 2026Sven Jacob, Bardh Prenkaj, Weijia Shao +1Change-Point Detection Sliced Wasserstein Distance
Federal Institute for Occupational Safety and Health, Dresden · Technical University of Munich, Munich Center for Machine Learning · Sapienza University of Rome
Distribution Steering via Sliced Optimal Transport Control
Aug 13, 2026Kaito Ito, Anqi DongFeedback Control Optimal Control
Department of Information Physics and Computing The University of Tokyo, Tokyo 113-8654, Japan · Department of Mathematics and Digital Futures KTH Royal Institute of Technology, 100 44 Stockholm, Sweden
Distributional Determinantal Point Process for Repulsive Clustering of Distributions
Jul 23, 2026Khai Nguyen, Yang Ni, Elizabeth Juarez-Colunga +1Bayesian Mixture Models Clustering
Department of Statistics and Data Sciences, University of Texas at Austin · Department of Biostatistics and Informatics, University of Colorado Anschutz · Department of Mathematics, University of Texas at Austin
Highly Data Parallelizable Estimation of the Sliced-Wasserstein Distance Using Cumulative Distribution Functions
Jun 29, 2026Christophe Vauthier, Quentin Mérigot, Anna KorbaSliced Wasserstein Distance Optimal Transport
Laboratoire de Mathématiques d’Orsay, Université Paris-Saclay Gif-sur-Yvette, France · Centre de recherche en économie et statistique, ENSAE Palaiseau, France
Adjusted Wasserstein distances for bridging empirical and true distributions with applications to MDS
Jun 29, 2026Flor Martinez-Sermeno, Arturo Jaramillo, Johan Van HorebeekDimensionality Reduction Heavy-Tailed Distributions
A Sliced-Wasserstein Framework on Correlation Matrices for EEG Decoding
Jun 4, 2026Chen Hu, Rui Wang, Jiale Zhou +4EEG Decoding Domain Generalization
Westlake University Hangzhou, China · School of Artificial Intelligence and Computer Science Jiangnan University Wuxi, China · Zhejiang University Hangzhou, China +1
VISReg: Variance-Invariance-Sketching Regularization for JEPA training
Jun 1, 2026Haiyu Wu, Randall Balestriero, Morgan LevineRepresentation Collapse Joint-Embedding Predictive Architecture
Altos Labs · Brown University
The Observable Wasserstein Distance
May 11, 2026Edivaldo Lopes dos Santos, Leandro Vicente Mauri, Washington Mio +1Sliced Wasserstein Distance Optimal Transport
1Departamento de Matem´atica, Universidade Federal de S˜ao Carlos, Brazil · Department of Mathematics, Florida State University, USA
APEX: Assumption-free Projection-based Embedding eXamination Metric for Image Quality Assessment
May 8, 2026Caterina Gallegati, Monica Bianchini, Franco Scarselli +2Image Quality Assessment Image Generation Evaluation
University of Siena, Italy · AI for Good (AIGO), Istituto Italiano di Tecnologia, Italy · University of Verona, Italy
MIND: Monge Inception Distance for Generative Models Evaluation
May 7, 2026Quentin Berthet, Yu-Han Wu, Clement Crepy +3Image Generation Evaluation Sliced Wasserstein Distance
Google DeepMind · LPSM, Sorbonne Université
Convergence Rates for Distribution Matching with Sliced Optimal Transport
Feb 11, 2026Gauthier Thurin, Claire Boyer, Kimia NadjahiDistribution Matching Sliced Wasserstein Distance
ENS-PSL · CNRS, ENS Paris, France · LMO, IUF, CELESTE +2

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High-Dimensional Online Change Point Detection with Adaptive Thresholding and Interpretability

Sven Jacob, Bardh Prenkaj, Weijia Shao, Gjergji Kasneci

Federal Institute for Occupational Safety and Health, Dresden · Technical University of Munich, Munich Center for Machine Learning · Sapienza University of Rome

Change-Point Detection Sliced Wasserstein Distance

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Change point detection (CPD) identifies abrupt and significant changes in sequential data, with applications in human activity recognition, financial markets, cybersecurity, manufacturing, and autonomous systems. Traditional CPD methods often face computational challenges in high-dimensional settings and typically provide limited explanations for detected changes, which can restrict their practical usability. This paper introduces a CPD framework that improves scalability and interpretability by leveraging the Sliced Wasserstein (SW) distance. Our contributions are fourfold: (1) we transform multivariate sequential data into one-dimensional scores using the SW distance, making the resulting representation compatible with existing CPD methods; (2) we analyze the distributional behavior of random slices of the SW distance and show that, under suitable assumptions, they can be approximated by a Gamma distribution, providing a principled basis for threshold calibration; (3) we propose a self-adapting online CPD algorithm that combines this SW-based score with an adaptive quantile-based threshold; (4) we introduce a model-specific framework for generating contrastive explanations for annotated change points. Empirically, our method reduces false positives by at least

48\%

on average compared with popular online and offline CPD baselines, while maintaining competitive or superior detection performance. Code is available at https://github.com/jsve96/SWCPD_Code. At the same time, it produces interpretable change-point annotations, making it practical for deployment in high-stakes applications.

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