{"total":153,"rows":[{"id":"2206.09426","title":"ADBench: Anomaly Detection Benchmark","created":"2022-06-19","cited_by_count":232,"area":null},{"id":"2303.00601","title":"Multimodal Industrial Anomaly Detection via Hybrid Fusion","created":"2023-03-01","cited_by_count":139,"area":null},{"id":"2206.06602","title":"Deep Isolation Forest for Anomaly Detection","created":"2022-06-14","cited_by_count":92,"area":null},{"id":"2206.10071","title":"BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs","created":"2022-06-21","cited_by_count":80,"area":null},{"id":"2305.18593","title":"On Diffusion Modeling for Anomaly Detection","created":"2023-05-29","cited_by_count":72,"area":"diffusion"},{"id":"2207.12201","title":"Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection","created":"2022-07-25","cited_by_count":40,"area":null},{"id":"2204.12095","title":"PyGOD: A Python Library for Graph Outlier Detection","created":"2022-04-26","cited_by_count":32,"area":null},{"id":"2412.11142","title":"AD-LLM: Benchmarking Large Language Models for Anomaly Detection","created":"2024-12-15","cited_by_count":26,"area":null},{"id":"2305.00735","title":"Unsupervised anomaly detection algorithms on real-world data: how many do we need?","created":"2023-05-01","cited_by_count":20,"area":null},{"id":"2412.04784","title":"NLP-ADBench: NLP Anomaly Detection Benchmark","created":"2024-12-06","cited_by_count":17,"area":null},{"id":"2311.11961","title":"NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly Generation","created":"2023-11-20","cited_by_count":14,"area":null},{"id":"2406.16308","title":"Anomaly Detection of Tabular Data Using LLMs","created":"2024-06-24","cited_by_count":12,"area":"lr"},{"id":"2206.15476","title":"AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detection","created":"2022-06-30","cited_by_count":11,"area":null},{"id":"2309.15376","title":"ADGym: Design Choices for Deep Anomaly Detection","created":"2023-09-27","cited_by_count":11,"area":null},{"id":"2305.13189","title":"Unsupervised Anomaly Detection with Rejection","created":"2023-05-22","cited_by_count":9,"area":null},{"id":"2502.11059","title":"ClimateLLM: Efficient Weather Forecasting via Frequency-Aware Large Language Models","created":"2025-02-16","cited_by_count":9,"area":"moe"},{"id":"2305.17048","title":"Intrinsic Self-Supervision for Data Quality Audits","created":"2023-05-26","cited_by_count":8,"area":null},{"id":"2306.00582","title":"Anomaly Detection with Variance Stabilized Density Estimation","created":"2023-06-01","cited_by_count":8,"area":null},{"id":"2306.01997","title":"UADB: Unsupervised Anomaly Detection Booster","created":"2023-06-03","cited_by_count":8,"area":null},{"id":"2506.09368","title":"Anomaly Detection and Generation with Diffusion Models: A Survey","created":"2025-06-11","cited_by_count":8,"area":"diffusion"},{"id":"2301.04257","title":"ODIM: Outlier Detection via Likelihood of Under-Fitted Generative Models","created":"2023-01-11","cited_by_count":7,"area":null},{"id":"2306.14403","title":"Anomaly Detection with Score Distribution Discrimination","created":"2023-06-26","cited_by_count":7,"area":null},{"id":"2309.01899","title":"Unsupervised Skin Lesion Segmentation via Structural Entropy Minimization on Multi-Scale Superpixel Graphs","created":"2023-09-05","cited_by_count":7,"area":null},{"id":"2407.04730","title":"The OPS-SAT benchmark for detecting anomalies in satellite telemetry","created":"2024-06-29","cited_by_count":7,"area":null},{"id":"2603.14171","title":"TACTIC for Navigating the Unknown: Tabular Anomaly deteCTion via In-Context inference","created":"2026-03-15","cited_by_count":7,"area":null},{"id":"2402.15988","title":"Towards Fair Graph Anomaly Detection: Problem, Benchmark Datasets, and Evaluation","created":"2024-02-25","cited_by_count":6,"area":null},{"id":"2501.11960","title":"TAD-Bench: A Comprehensive Benchmark for Embedding-Based Text Anomaly Detection","created":"2025-01-21","cited_by_count":6,"area":null},{"id":"2504.21247","title":"Subject Information Extraction for Novelty Detection with Domain Shifts","created":"2025-04-30","cited_by_count":6,"area":null},{"id":"2204.01707","title":"Quadratic Neuron-empowered Heterogeneous Autoencoder for Unsupervised Anomaly Detection","created":"2022-04-02","cited_by_count":5,"area":null},{"id":"2306.02421","title":"Auto-Validate by-History: Auto-Program Data Quality Constraints to Validate Recurring Data Pipelines","created":"2023-06-04","cited_by_count":5,"area":null},{"id":"2307.12336","title":"TabADM: Unsupervised Tabular Anomaly Detection with Diffusion Models","created":"2023-07-23","cited_by_count":5,"area":"diffusion"},{"id":"2310.05468","title":"Enhancing Interpretability and Generalizability in Extended Isolation Forests","created":"2023-10-09","cited_by_count":5,"area":null},{"id":"2412.02000","title":"Who's Gaming the System? A Causally-Motivated Approach for Detecting Strategic Adaptation","created":"2024-12-02","cited_by_count":5,"area":null},{"id":"2503.23775","title":"Evaluation of (Un-)Supervised Machine Learning Methods for GNSS Interference Classification with Real-World Data Discrepancies","created":"2025-03-31","cited_by_count":5,"area":null},{"id":"2505.19543","title":"Cuff-KT: Tackling Learners' Real-time Learning Pattern Adjustment via Tuning-Free Knowledge State Guided Model Updating","created":"2025-05-26","cited_by_count":5,"area":null},{"id":"2510.13197","title":"Text Anomaly Detection with Simplified Isolation Kernel","created":"2025-10-15","cited_by_count":5,"area":null},{"id":"2205.09884","title":"Time Series Anomaly Detection via Reinforcement Learning-Based Model Selection","created":"2022-05-19","cited_by_count":4,"area":null},{"id":"2209.07957","title":"Malicious Source Code Detection Using Transformer","created":"2022-09-16","cited_by_count":4,"area":null},{"id":"2312.13896","title":"Comparative Evaluation of Anomaly Detection Methods for Fraud Detection in Online Credit Card Payments","created":"2023-12-21","cited_by_count":4,"area":null},{"id":"2403.03698","title":"Towards Controllable Time Series Generation","created":"2024-03-06","cited_by_count":4,"area":null},{"id":"2407.02662","title":"Supporters and Skeptics: LLM-based Analysis of Engagement with Mental Health (Mis)Information Content on Video-sharing Platforms","created":"2024-07-02","cited_by_count":4,"area":null},{"id":"2409.18427","title":"Neural Collaborative Filtering to Detect Anomalies in Human Semantic Trajectories","created":"2024-09-27","cited_by_count":4,"area":null},{"id":"2411.07574","title":"Disentangling Tabular Data Towards Better One-Class Anomaly Detection","created":"2024-11-12","cited_by_count":4,"area":null},{"id":"2502.12793","title":"Unsupervised Anomaly Detection through Mass Repulsing Optimal Transport","created":"2025-02-18","cited_by_count":4,"area":null},{"id":"2507.15584","title":"We Need to Rethink Benchmarking in Anomaly Detection","created":"2025-07-21","cited_by_count":4,"area":null},{"id":"2508.00758","title":"Diffusion-Scheduled Denoising Autoencoders for Anomaly Detection in Tabular Data","created":"2025-08-01","cited_by_count":4,"area":null},{"id":"2603.19497","title":"ICLAD: In-Context Learning for Unified Tabular Anomaly Detection Across Supervision Regimes","created":"2026-03-19","cited_by_count":4,"area":null},{"id":"2603.23205","title":"Between Resolution Collapse and Variance Inflation: Weighted Conformal Anomaly Detection in Low-Data Regimes","created":"2026-03-24","cited_by_count":4,"area":null},{"id":"2204.10888","title":"Capturing the Denoising Effect of PCA via Compression Ratio","created":"2022-04-22","cited_by_count":3,"area":null},{"id":"2306.12703","title":"OptIForest: Optimal Isolation Forest for Anomaly Detection","created":"2023-06-22","cited_by_count":3,"area":null},{"id":"2308.01063","title":"Graph Anomaly Detection at Group Level: A Topology Pattern Enhanced Unsupervised Approach","created":"2023-08-02","cited_by_count":3,"area":null},{"id":"2308.10504","title":"Adaptive Thresholding Heuristic for KPI Anomaly Detection","created":"2023-08-21","cited_by_count":3,"area":null},{"id":"2308.15059","title":"OEBench: Investigating Open Environment Challenges in Real-World Relational Data Streams","created":"2023-08-29","cited_by_count":3,"area":null},{"id":"2402.04567","title":"OIL-AD: An Anomaly Detection Framework for Sequential Decision Sequences","created":"2024-02-07","cited_by_count":3,"area":"offlinerl"},{"id":"2412.08501","title":"GradStop: Exploring Training Dynamics in Unsupervised Outlier Detection through Gradient","created":"2024-12-11","cited_by_count":3,"area":null},{"id":"2504.20733","title":"Unsupervised Surrogate Anomaly Detection","created":"2025-04-29","cited_by_count":3,"area":null},{"id":"2601.17786","title":"Beyond a Single Perspective: Text Anomaly Detection with Multi-View Language Representations","created":"2026-01-25","cited_by_count":3,"area":"safety"},{"id":"2608.22808","title":"CatchBench: When Can an Agent Failure Be Caught?","created":"2026-08-24","cited_by_count":3,"area":null},{"id":"2201.04014","title":"Captcha Attack: Turning Captchas Against Humanity","created":"2022-01-11","cited_by_count":2,"area":null},{"id":"2307.14387","title":"Coupled-Space Attacks against Random-Walk-based Anomaly Detection","created":"2023-07-26","cited_by_count":2,"area":null},{"id":"2309.00379","title":"Anomaly detection with semi-supervised classification based on risk estimators","created":"2023-09-01","cited_by_count":2,"area":null},{"id":"2309.07461","title":"Detecting Unknown Attacks in IoT Environments: An Open Set Classifier for Enhanced Network Intrusion Detection","created":"2023-09-14","cited_by_count":2,"area":null},{"id":"2401.00773","title":"Unsupervised Outlier Detection using Random Subspace and Subsampling Ensembles of Dirichlet Process Mixtures","created":"2024-01-01","cited_by_count":2,"area":null},{"id":"2403.01245","title":"AcME-AD: Accelerated Model Explanations for Anomaly Detection","created":"2024-03-02","cited_by_count":2,"area":null},{"id":"2405.19823","title":"Joint Selective State Space Model and Detrending for Robust Time Series Anomaly Detection","created":"2024-05-30","cited_by_count":2,"area":null},{"id":"2504.07522","title":"Adversarial Subspace Generation for Outlier Detection in High-Dimensional Data","created":"2025-04-10","cited_by_count":2,"area":null},{"id":"2504.11623","title":"Possibility for Proactive Anomaly Detection","created":"2025-04-15","cited_by_count":2,"area":null},{"id":"2505.11034","title":"CleanPatrick: A Benchmark for Image Data Cleaning","created":"2025-05-16","cited_by_count":2,"area":null},{"id":"2601.13644","title":"Towards Token-Level Text Anomaly Detection","created":"2026-01-20","cited_by_count":2,"area":null},{"id":"2606.01300","title":"ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection","created":"2026-05-31","cited_by_count":2,"area":null},{"id":"2606.12483","title":"Scalable anomaly detection via a univariate Christoffel function","created":"2026-06-10","cited_by_count":2,"area":null},{"id":"2302.07849","title":"Zero-Shot Anomaly Detection via Batch Normalization","created":"2023-02-15","cited_by_count":1,"area":null},{"id":"2303.09972","title":"Neighborhood Averaging for Improving Outlier Detectors","created":"2023-03-17","cited_by_count":1,"area":null},{"id":"2304.03220","title":"Anomaly Detection via Gumbel Noise Score Matching","created":"2023-04-06","cited_by_count":1,"area":null},{"id":"2305.05827","title":"Inclusive FinTech Lending via Contrastive Learning and Domain Adaptation","created":"2023-05-10","cited_by_count":1,"area":null},{"id":"2312.02762","title":"Learning Cortical Anomaly through Masked Encoding for Unsupervised Heterogeneity Mapping","created":"2023-12-05","cited_by_count":1,"area":null},{"id":"2312.12010","title":"Outlier detection using flexible categorisation and interrogative agendas","created":"2023-12-19","cited_by_count":1,"area":null},{"id":"2404.03495","title":"Deep Transductive Outlier Detection","created":"2024-04-04","cited_by_count":1,"area":null},{"id":"2406.00452","title":"Towards a Unified Framework of Clustering-based Anomaly Detection","created":"2024-06-01","cited_by_count":1,"area":null},{"id":"2408.09791","title":"ALTBI: Constructing Improved Outlier Detection Models via Optimization of Inlier-Memorization Effect","created":"2024-08-19","cited_by_count":1,"area":null},{"id":"2408.15012","title":"Flexible categorization using formal concept analysis and Dempster-Shafer theory","created":"2024-08-23","cited_by_count":1,"area":null},{"id":"2408.15874","title":"Robust Statistical Scaling of Outlier Scores: Improving the Quality of Outlier Probabilities for Outliers (Extended Version)","created":"2024-08-28","cited_by_count":1,"area":null},{"id":"2410.14579","title":"Towards Unsupervised Validation of Anomaly-Detection Models","created":"2024-10-18","cited_by_count":1,"area":null},{"id":"2503.06413","title":"Swift Hydra: Self-Reinforcing Generative Framework for Anomaly Detection with Multiple Mamba Models","created":"2025-03-09","cited_by_count":1,"area":"moe"},{"id":"2504.16075","title":"Explainable Unsupervised Anomaly Detection with Random Forest","created":"2025-04-22","cited_by_count":1,"area":null},{"id":"2504.20906","title":"A Giant-Step Baby-Step Classifier For Scalable and Real-Time Anomaly Detection In Industrial Control Systems and Water Treatment Systems","created":"2025-04-29","cited_by_count":1,"area":null},{"id":"2507.12295","title":"Text-ADBench: Text Anomaly Detection Benchmark Based on LLM Embeddings","created":"2025-07-16","cited_by_count":1,"area":null},{"id":"2510.03904","title":"LLM as an Algorithmist: Enhancing Anomaly Detectors via Programmatic Synthesis","created":"2025-10-04","cited_by_count":1,"area":null},{"id":"2511.03095","title":"Sparse, self-organizing ensembles of local kernels detect rare statistical anomalies","created":"2025-11-05","cited_by_count":1,"area":null},{"id":"2512.07863","title":"SetAD: Semi-Supervised Anomaly Learning in Contextual Sets","created":"2025-11-26","cited_by_count":1,"area":null},{"id":"2602.07798","title":"CausalTAD: Injecting Causal Knowledge into Large Language Models for Tabular Anomaly Detection","created":"2026-02-08","cited_by_count":1,"area":null},{"id":"2603.12847","title":"Hierarchical Reference Sets for Robust Unsupervised Detection of Scattered and Clustered Outliers","created":"2026-03-13","cited_by_count":1,"area":null},{"id":"2605.26468","title":"Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection","created":"2026-05-26","cited_by_count":1,"area":"diffusion"},{"id":"2607.20530","title":"CLOE: Christoffel Loss Autoencoder for Anomaly Detection","created":"2026-07-10","cited_by_count":1,"area":null},{"id":"2304.00709","title":"MSS-PAE: Saving Autoencoder-based Outlier Detection from Unexpected Reconstruction","created":"2023-04-03","cited_by_count":0,"area":null},{"id":"2305.00982","title":"Two-phase Dual COPOD Method for Anomaly Detection in Industrial Control System","created":"2023-04-30","cited_by_count":0,"area":null},{"id":"2305.18389","title":"AnoRand: A Semi Supervised Deep Learning Anomaly Detection Method by Random Labeling","created":"2023-05-28","cited_by_count":0,"area":null},{"id":"2308.12925","title":"Low-count Time Series Anomaly Detection","created":"2023-08-24","cited_by_count":0,"area":null},{"id":"2309.05630","title":"Boundary Peeling: Outlier Detection Method Using One-Class Peeling","created":"2023-09-11","cited_by_count":0,"area":null},{"id":"2311.05473","title":"Do Ensembling and Meta-Learning Improve Outlier Detection in Randomized Controlled Trials?","created":"2023-11-09","cited_by_count":0,"area":null}],"next_cursor":"100"}