CLOE: Christoffel Loss Autoencoder for Anomaly Detection

Semi-supervised anomaly detection plays a key role in diverse fields such as process monitoring, healthcare, and finance. However, lightweight methods often struggle with high-dimensional data and typically require careful tuning of multiple hyperparameters. Among existing approaches, Christoffel Function--based methods are attractive due to their simplicity, requiring at most a single hyperparameter. They also benefit from a well-established theoretical foundation that yields several interesting results for data science. However, their main limitation is poor scalability to high-dimensional settings. In this paper, we introduce CLOE, a new method that combines an autoencoder for dimensionality reduction with a Christoffel Function--based detector applied in the latent space. To better align representation learning with anomaly detection, we design a novel loss function that leverages the Christoffel Function to guide the autoencoder toward representations that better capture the support of the normal data distribution. We further propose a principled procedure to set the detection threshold and an efficient strategy to tune the single remaining hyperparameter. Experiments on multiple high-dimensional tabular anomaly detection benchmarks demonstrate that CLOE achieves superior performance compared to existing methods, while preserving the lightweight and low-tuning advantages of Christoffel Function--based approaches.

Sum-of-Squares Degree Barriers for the Reweighted-Hinge Method in Robust Halfspace Learning: A Christoffel-Function Characterization

A certificate that removes outliers sees the data only through its low-degree moments, and an adversary exploits exactly this, hiding corruption where the clean data already looks typical, in the blind spot no bounded-degree test resolves. That blind spot has an exact size: the Christoffel function of the clean marginal, the quantity data analysis thresholds to detect outliers, here read from the adversary's side as the corruption a certificate cannot remove. We turn this inversion into the organizing principle of the reweighted-hinge approach to robustly learning $γ$-margin halfspaces under malicious noise (Shen 2025; Zeng-Shen 2025): the governing resource is the Sum-of-Squares degree of the certificate, and the resolution principle states that the maximal corruption mass hideable at a center $c$ from a degree-$2t$ certificate is exactly the Christoffel function $λ_{t+1}(c)$. Three consequences follow, all against the certificate method (not information-theoretic). A margin-degree tradeoff: certifying the dense pancake to error $\varepsilon$ costs SoS degree $Ω(\log(1/\varepsilon))$ or margin $Ω(\sqrt{\log(1/\varepsilon)}/\sqrt{d})$, so the $\log(1/\varepsilon)$ margin of Shen (2025) is forced; a weighted-Chebyshev reduction makes the threshold $2t=Θ((|c|/s)^2)$ tight modulo one classical extremal estimate. A degree-2 outlier barrier: an explicit instance on which degree 2 is stuck at $η^{1/2}$ while degree 4 escapes, locating the small breakdown rate in the degree, not the analysis. A degree-$2t$ algorithm tracing the frontier $η^{1-1/2t}$ (recovering Shen 2025 at $t=1$), with an explicit constant gain capped by the pancake density. And an information-theoretic floor of $η/(2(1-η))$, matched exactly from above; under a hard margin its two-point realizations provably require $Θ(1/η)$ mixture components."