arXiv Machine Learning
RankShift: In-Database Detection and Explanation of Categorical Shifts
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CRAFTIIF: Cross-Resolution Analytic Four-Type Interpretable Isolation Forest for Multivariate Time Series Anomaly Detection
arXiv:2606. 13486v1 Announce Type: cross Abstract: Anomaly detection in multivariate time series is challenged by four structurally distinct anomaly types -- point (isolated spikes), distributional (level shifts), temporal (rhythm changes), and collective (inter-sensor correlation breakdowns) -- each requiring different feature representations.
arXiv Machine Learning
Jul 21
When Drift Detectors cry Wolf: False Alarm Rates in continuous ML Monitoring
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TriCalRAG: A Three-Strategy, Retrieval-Augmented Benchmark for On-Premise LLM-Based Root Cause Analysis in AIOps
arXiv:2609.14762v1 Announce Type: cross Abstract: Cloud-hosted large language models (LLMs) are increasingly used for root cause analysis (RCA) in AIOps pipelines, but they introduce data privacy ris...
arXiv Machine Learning
Aug 14
Dimension-Calibrated Unexplained Mass: An Interpretable Drift Statistic for Contamination Monitoring in Data Streams
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 Machine Learning
Jul 21
FAME: Failure-Aware Mixture-of-Experts for Message-Level Log Anomaly Detection
arXiv:2605. 22779v2 Announce Type: replace-cross Abstract: Production systems generate millions of log lines daily, yet most anomaly detectors operate at the session or window-level, flagging groups of lines rather than identifying the specific message responsible.
arXiv Machine Learning
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Risk-Calibrated Bayesian Streaming Intrusion Detection with SRE-Aligned Decisions
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