The paper evaluates five multivariate two-sample drift detectors on large‑scale e‑commerce data, including a 137.5‑million‑row Trendyol feature table. Distributed Maximum Mean Discrepancy with Random Fourier Features on Apache Spark shows strong performance, achieving a Pearson correlation of 0.940 with expected drift magnitude, an 80.4% true positive rate, and a 3.2% false positive rate. In contrast, the per‑dimension Kolmogorov‑Smirnov test fails due to statistic saturation from ID‑like columns, and detectors struggle under weak drift configurations, underscoring limitations in current sampling designs and sensitivity analysis.
arXiv:2606. 07789v1 Announce Type: new Abstract: Data stream mining is fundamentally challenged by concept drift, where distributional changes can degrade model performance.
arXiv:2607. 17336v1 Announce Type: new Abstract: Drift detection is a core component of production machine learning monitoring systems, where detectors are used to compare incoming data with a reference distribution and trigger alerts when changes occur.
arXiv:2606. 20216v1 Announce Type: cross Abstract: Machine learning algorithms deployed for evolving streaming environments must handle the non-stationary data distributions, commonly referred to as concept drift.
arXiv:2607. 16811v4 Announce Type: replace Abstract: Drift detectors that work tend not to explain themselves, and drift detectors that explain themselves tend to fail in high dimension.
arXiv:2607. 16811v3 Announce Type: replace Abstract: Drift detectors that work tend not to explain themselves, and drift detectors that explain themselves tend to fail in high dimension.
arXiv:2606. 03305v1 Announce Type: new Abstract: Benchmark contamination, where evaluation examples appear in a model's training data, threatens the validity of LLM assessment.
arXiv:2607. 16811v1 Announce Type: new Abstract: We revisit Gaussian Mixture Models (GMMs) as a lightweight, interpretable tool for anomaly detection and, in particular, for detecting distributional drift in data streams.
arXiv:2608. 14186v1 Announce Type: new Abstract: Tabular anomaly detection is dominated by classical density-proxy methods (Isolation Forest, OCSVM, LOF), reconstruction-based detectors (Autoencoders, VAEs), and modern non-parametric scorers (COPOD, ECOD, Deep SVDD), all of which approximate the inlier distribution only indirectly; explicit energy-based models are largely absent.
The paper investigates how repeated rows in released datasets—often treated as i.i.d. samples—introduce a hidden measurement layer that affects anomaly detection. It shows that identical rows can cap evaluation performance, make AUROC sensitive to replication, and bias detectors toward multiplicity size. The authors audit 690 OddBench datasets, find significant train-test overlap and label conflicts, and propose SCOUT, a support‑count orthogonalized detector that separates replication‑invariant evidence from exposure‑aware counts, achieving comparable or better AUROC while controlling false‑positive rates.
The paper introduces a framework for out-of-distribution (OOD) detection that addresses the trade‑off between detection performance and classification accuracy caused by fine‑tuning with auxiliary outlier data. It optimizes three factors—model reminder, data sampling, and representation learning—by proposing Self‑Knowledge Distillation to preserve accuracy, Semi‑hard Outlier Sampling to enhance detection with minimal data, and Outlier‑aware Supervised Contrastive Learning to improve ID‑OOD separability. The combined approach yields cumulative gains, outperforming existing methods on diverse benchmarks, especially in long‑tailed scenarios, and offers a robust baseline for real‑world OOD detection.
INCONVENIENT is a new benchmark that evaluates how machine‑learning models, especially large language models, generalize when trained on convenience samples that bias feature–target relationships. Unlike prior distribution‑shift tests that rely on natural domain splits, INCONVENIENT explicitly perturbs these relationships while keeping the deployment population unchanged, allowing controlled study of shift severity and direction. The benchmark covers 200 tabular classification and regression datasets and produces about 15,500 shifted training distributions, revealing significant performance degradation.
arXiv:2606. 08460v1 Announce Type: cross Abstract: Data-adaptive two-sample testing assesses if two samples come from the same distribution, using a discrepancy learned from the data (e.