Mathias Verbeke

AAAI Conference 2026 System Paper

InTimeAD: Interactive Time Series Anomaly Detection

• Louis Carpentier
• Wannes Meert
• Mathias Verbeke

Time series anomaly detection has received substantial attention over the past two decades, leading to the development of hundreds of algorithms. However, comprehensively understanding this vast landscape remains challenging, particularly for non-experts and novices. In this demonstration paper, we present InTimeAD, an interactive web application that provides access to more than 30 state-of-the-art time series anomaly detection algorithms. InTimeAD is intended to explore the performance of existing as well as custom anomaly detection models in an interactive, hands-on manner. By lowering the entry bar, we support practitioners overwhelmed by the large number of existing techniques, while providing a platform for researchers to rapidly analyze their novel anomaly detection algorithms.

AAAI Conference 2026 Conference Paper

Koopman Invariants as Drivers of Emergent Time-Series Clustering in Joint-Embedding Predictive Architectures

• Pablo Ruiz-Morales
• Dries Vanoost
• Davy Pissoort
• Mathias Verbeke

Joint-Embedding Predictive Architectures (JEPAs), a powerful class of self-supervised models, exhibit an unexplained ability to cluster time-series data by their underlying dynamical regimes. We propose a novel theoretical explanation for this phenomenon, hypothesizing that JEPA's predictive objective implicitly drives it to learn the invariant subspace of the system's Koopman operator. We prove that an idealized JEPA loss is minimized when the encoder represents the system's regime indicator functions, which are Koopman eigenfunctions. This theory was validated on synthetic data with known dynamics, demonstrating that constraining the JEPA's linear predictor to be a near-identity operator is the key inductive bias that forces the encoder to learn these invariants. We further discuss that this constraint is critical for selecting this interpretable solution from a class of mathematically equivalent but entangled optima, revealing the predictor's role in representation disentanglement. This work demystifies a key behavior of JEPAs, provides a principled connection between modern self-supervised learning and dynamical systems theory, and informs the design of more robust and interpretable time-series models.

EAAI Journal 2025 Journal Article

Data-driven models with physical interpretability for real-time cavity profile prediction in electrochemical machining processes

• Ming Wu
• Zequan Yao
• Mathias Verbeke
• Peter Karsmakers
• Benjamin Gorissen
• Dominiek Reynaerts

IJCAI Conference 2015 Conference Paper

Inducing Probabilistic Relational Rules from Probabilistic Examples

• Luc De Raedt
• Anton Dries
• Ingo Thon
• Guy Van den Broeck
• Mathias Verbeke

We study the problem of inducing logic programs in a probabilistic setting, in which both the example descriptions and their classification can be probabilistic. The setting is incorporated in the probabilistic rule learner ProbFOIL+, which combines principles of the rule learner FOIL with ProbLog, a probabilistic Prolog. We illustrate the approach by applying it to the knowledge base of NELL, the Never-Ending Language Learner.