mlsanityData to 7 Oct 2026

PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection

arXiv 2412.12154 ↗First posted 11 Dec 2024cs.LG cs.AI cs.CLcited by 17

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Abstract (arXiv)

Outlier detection (OD), also known as anomaly detection, is a critical machine learning (ML) task with applications in fraud detection, network intrusion detection, clickstream analysis, recommendation systems, and social network moderation. Among open-source libraries for outlier detection, the Python Outlier Detection (PyOD) library is the most widely adopted, with over 8,500 GitHub stars, 25 million downloads, and diverse industry usage. However, PyOD currently faces three limitations: (1) insufficient coverage of modern deep learning algorithms, (2) fragmented implementations across PyTorch and TensorFlow, and (3) no automated model selection, making it hard for non-experts. To address these issues, we present PyOD Version 2 (PyOD 2), which integrates 12 state-of-the-art deep learning models into a unified PyTorch framework and introduces a large language model (LLM)-based pipeline for automated OD model selection. These improvements simplify OD workflows, provide access to 45 algorithms, and deliver robust performance on various datasets. In this paper, we demonstrate how PyOD 2 streamlines the deployment and automation of OD models and sets a new standard in both research and industry. PyOD 2 is accessible at [https://github.com/yzhao062/pyod](https://github.com/yzhao062/pyod). This study aligns with the Web Mining and Content Analysis track, addressing topics such as the robustness of Web mining methods and the quality of algorithmically-generated Web data.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 12 of its 23 references that are in the map. First posted on arXiv 11 Dec 2024.

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12 of 23 references.

References · 12

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2022ADBench: Anomaly Detection Benchmarkcited by 230
  3. 2019PyOD: A Python Toolbox for Scalable Outlier Detectioncited by 221
  4. 2019Deep Anomaly Detection with Deviation Networkscited by 152
  5. 2018Adversarially Learned Anomaly Detectioncited by 115
  6. 2022BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphscited by 80
  7. 2018Generative Adversarial Active Learning for Unsupervised Outlier Detectioncited by 65
  8. 2021LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networkscited by 42
  9. 2023Panoptic Scene Graph Generation with Semantics-Prototype Learningcited by 42
  10. 2020AutoAudit: Mining Accounting and Time-Evolving Graphscited by 15
  11. 2022ADMoE: Anomaly Detection with Mixture-of-Experts from Noisy Labelscited by 15
  12. 2018Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier featurescited by 14

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 7; 2026: 9.

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  1. 2024AD-LLM: Benchmarking Large Language Models for Anomaly Detectioncited by 25
  2. 2025SymRTLO: Enhancing RTL Code Optimization with LLMs and Neuron-Inspired Symbolic Reasoningcited by 16
  3. 2026FORTIS: Benchmarking Over-Privilege in Agent Skillscited by 11
  4. 2025ClimateLLM: Efficient Weather Forecasting via Frequency-Aware Large Language Modelscited by 9
  5. 2026The Autonomy Tax: Defense Training Breaks LLM Agentscited by 8
  6. 2025AD-AGENT: A Multi-agent Framework for End-to-end Anomaly Detectioncited by 7
  7. 2026ICLAD: In-Context Learning for Unified Tabular Anomaly Detection Across Supervision Regimescited by 3
  8. 2025Machine Learning-Driven Anomaly Detection for 5G O-RAN Performance Metricscited by 1
  9. 2025Detecting Spelling and Grammatical Anomalies in Russian Poetry Textscited by 0
  10. 2025MR-EEGWaveNet: Multiresolutional EEGWaveNet for Seizure Detection from Long EEG Recordingscited by 0
  11. 2025UniOD: A Universal Model for Outlier Detection across Diverse Domainscited by 0
  12. 2026Anatomy of a failure: When, how, and why deep vision fails in scientific domainscited by 0
5 more
  1. 2026Agent Safety Is Action Alignmentcited by 0
  2. 2026RGLD: Randomized Global-Local Density Estimation for Tabular Anomaly Detectioncited by 0
  3. 2026Real-time and adaptive anomaly detection algorithm for cyclostationary modelscited by 0
  4. 2026TaskBridge: Bridging Unsupervised Tabular Anomaly Detection and In-Context Learning via Virtual Taskscited by 0
  5. 2026Graph Anomaly Detection as Finite-Horizon Control: Training-Free Scoring via Empirical Bayescited by 0