CLOE: Christoffel Loss Autoencoder for Anomaly Detection

arXiv:2607.20530 · cs.LG, cs.AI, stat.ML · Submitted 2026-07-10 · Read on arXiv

Léa Billet, Louise Travé-Massuyès, Elodie Chanthery, Alexandre Gaffet

LAAS, INSA Toulouse, ANITI · LAAS-DISCO, Comue de Toulouse, ANITI · LAAS

cs.LG, cs.AI, stat.ML

Submitted: 2026-07-10

Journal ref: The 16th International Conference on Information Science and Technology, University of Coimbra; City University of Hong Kong, Oct 2026, Coimbra, Portugal

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Christoffel Function — 20× in this paper
• Latent Space — 18× in this paper · explained in 7 episodes
• Clustering — 12× in this paper · explained in 2 episodes
• AUC — 8× in this paper · explained in 2 episodes
• Reconstruction Error — 6× in this paper · explained in 2 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Ablation Study — 3× in this paper · explained in 3 episodes
• Hyperparameters — 3× in this paper · explained in 2 episodes
• Representation Learning — 3× in this paper · explained in 3 episodes
• Decoder — 2× in this paper · explained in 2 episodes
• Dimensionality Reduction — 2× in this paper · explained in 2 episodes
• Latent Representation — 2× in this paper · explained in 2 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• Papers — 2× in this paper · explained in 3 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Scoring Function — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 3 episodes
• Curse of Dimensionality — 1× in this paper · explained in 6 episodes
• Mean Square Error — 1× in this paper
• Robustness — 1× in this paper · explained in 6 episodes
• Semi-supervised Learning — 1× in this paper · explained in 4 episodes

Sources

• Scalable anomaly detection via a univariate Christoffel function

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: The Predictive Cost of ELBO Binding
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks