# Kernel Change-point Detection with Auxiliary Deep Generative Models

arXiv: https://arxiv.org/abs/1901.06077 · HTML: https://mlsanity.com/paper/1901.06077/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jan 2019.

## Built on by (most cited first, 30 of 33)

- [Random Forests for Change Point Detection](https://mlsanity.com/paper/2205.04997/) (2022)
- [Automatic Change-Point Detection in Time Series via Deep Learning](https://mlsanity.com/paper/2211.03860/) (2022)
- [Diverse Sample Generation: Pushing the Limit of Generative Data-free Quantization](https://mlsanity.com/paper/2109.00212/) (2021)
- [Online Kernel CUSUM for Change-Point Detection](https://mlsanity.com/paper/2211.15070/) (2022)
- [Memory-free Online Change-point Detection: A Novel Neural Network Approach](https://mlsanity.com/paper/2207.03932/) (2022)
- [InDiD: Instant Disorder Detection via Representation Learning](https://mlsanity.com/paper/2106.02602/) (2021)
- [Detecting Change Intervals with Isolation Distributional Kernel](https://mlsanity.com/paper/2212.14630/) (2022)
- [Selective Inference for Changepoint detection by Recurrent Neural Network](https://mlsanity.com/paper/2311.14964/) (2023)
- [Unsupervised Detection of Behavioural Drifts with Dynamic Clustering and Trajectory Analysis](https://mlsanity.com/paper/2302.06228/) (2023)
- [On Rank Energy Statistics via Optimal Transport: Continuity, Convergence, and Change Point Detection](https://mlsanity.com/paper/2302.07964/) (2023)
- [Harnessing Synthetic Data from Generative AI for Statistical Inference](https://mlsanity.com/paper/2603.05396/) (2026)
- [Latent Neural Stochastic Differential Equations for Change Point Detection](https://mlsanity.com/paper/2208.10317/) (2022)
- [Normalizing self-supervised learning for provably reliable Change Point Detection](https://mlsanity.com/paper/2410.13637/) (2024)
- [Learning Sinkhorn divergences for supervised change point detection](https://mlsanity.com/paper/2202.04000/) (2022)
- [Self-Supervised Transformer Architecture for Change Detection in Radio Access Networks](https://mlsanity.com/paper/2302.02025/) (2023)
- [Online Change Points Detection for Linear Dynamical Systems with Finite Sample Guarantees](https://mlsanity.com/paper/2311.18769/) (2023)
- [Unsupervised Text Segmentation via Kernel Change-Point Detection on Sentence Embeddings](https://mlsanity.com/paper/2601.18788/) (2026)
- [Robust and efficient change point detection using novel multivariate rank-energy GoF test](https://mlsanity.com/paper/2111.00047/) (2021)
- [Improving Model Compatibility of Generative Adversarial Networks by Boundary Calibration](https://mlsanity.com/paper/2111.02316/) (2021)
- [Cadence: A Practical Time-series Partitioning Algorithm for Unlabeled IoT Sensor Streams](https://mlsanity.com/paper/2112.03360/) (2021)
- [Noise-contrastive Online Change Point Detection](https://mlsanity.com/paper/2206.10143/) (2022)
- [Dynamic Interpretable Change Point Detection](https://mlsanity.com/paper/2211.03991/) (2022)
- [Distribution estimation and change-point estimation for time series via DNN-based GANs](https://mlsanity.com/paper/2211.14577/) (2022)
- [RIO-CPD: A Riemannian Geometric Method for Correlation-aware Online Change Point Detection](https://mlsanity.com/paper/2407.09698/) (2024)
- [Real-time Fuel Leakage Detection via Online Change Point Detection](https://mlsanity.com/paper/2410.09741/) (2024)
- [Neural Network-Based Change Point Detection for Large-Scale Time-Evolving Data](https://mlsanity.com/paper/2503.09541/) (2025)
- [WWAggr: A Window Wasserstein-based Aggregation for Ensemble Change Point Detection](https://mlsanity.com/paper/2506.08066/) (2025)
- [Consistent Kernel Change-Point Detection under m-Dependence for Text Segmentation](https://mlsanity.com/paper/2510.03437/) (2025)
- [An Unsupervised Time Series Anomaly Detection Approach for Efficient Online Process Monitoring of Additive Manufacturing](https://mlsanity.com/paper/2510.09977/) (2025)
- [EvoTS-Agent: A Self-Evolving LLM Agent for Financial Time Series Change Point Detection](https://mlsanity.com/paper/2608.17933/) (2026)

## Abstract (arXiv)

Detecting the emergence of abrupt property changes in time series is a challenging problem. Kernel two-sample test has been studied for this task which makes fewer assumptions on the distributions than traditional parametric approaches. However, selecting kernels is non-trivial in practice. Although kernel selection for two-sample test has been studied, the insufficient samples in change point detection problem hinder the success of those developed kernel selection algorithms. In this paper, we propose KL-CPD, a novel kernel learning framework for time series CPD that optimizes a lower bound of test power via an auxiliary generative model. With deep kernel parameterization, KL-CPD endows kernel two-sample test with the data-driven kernel to detect different types of change-points in real-world applications. The proposed approach significantly outperformed other state-of-the-art methods in our comparative evaluation of benchmark datasets and simulation studies.