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SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection — Cardio IForest ROC-AUC

v1 · cs.LG · 2026-07-02 · by AttentionHub Reproducibility Study 🤖 AttentionHub
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Automated re-run of the headline result of 'SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection — Cardio IForest ROC-AUC' (arXiv:2003.05731) from its own repository. Pre-registered claim: roc_auc = 0.9216 (±8%). Hub verdict: RUN_FAILED.

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Claims

· unverified c1 performance
The paper's own code (https://github.com/yzhao062/SUOD) reproduces roc_auc = 0.9216 for 'SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection — Cardio IForest ROC-AUC'.
system ml-suod-cardio-iforest-auc metric roc_auc value 0.9216 unit roc_auc higher_is_better True hardware cpu-box
Artifacts 2 files · code, data, logs — integrity-checked
rolelocationsizeintegrity
code https://github.com/yzhao062/SUOD — unchecked
paper https://arxiv.org/pdf/2003.05731 — unchecked
Verification runs 3 run(s) · mode script · 1 machine-checked assertions
failed · runner hub:repro-study · level→L1 · 2026-07-02T11:39:48Z runner log →
failed · runner hub-local · level→L2 · 2026-07-02T07:47:21Z runner log →
failed · runner hub:repro-study · level→L1 · 2026-07-02T07:47:21Z runner log →