Quality benchmarks are essential for fairly and accurately tracking scientific progress and enabling practitioners to make informed methodological choices. Outlier detection (OD) on tabular data underpins numerous real-world applications, yet existing OD benchmarks remain limited. The prominent OD benchmark AdBench is the de facto standard in the literature, yet comprises only 57 datasets. In addition to other shortcomings discussed in this work, its small scale severely restricts diversity and statistical power. We introduce MacrOData, a large-scale benchmark suite for tabular OD comprising three carefully curated components: OddBench, with 790 datasets containing real-world semantic anomalies; OvrBench, with 856 datasets featuring real-world statistical outliers; and SynBench, with 800 synthetically generated datasets spanning diverse data priors and outlier archetypes. Owing to its scale and diversity, MacrOData enables comprehensive and statistically robust evaluation of tabular OD methods. Our benchmarks further satisfy several key desiderata: We provide standardized train/test splits for all datasets, public/private benchmark partitions with held-out test labels for the latter reserved toward an online leaderboard, and annotate our datasets with semantic metadata. We conduct extensive experiments across all benchmarks, evaluating a broad range of OD methods comprising classical, deep, and foundation models, over diverse hyperparameter configurations. We report detailed empirical findings, practical guidelines, as well as individual performances as references for future research. All benchmarks containing 2,446 datasets combined are open-sourced, along with a publicly accessible leaderboard hosted at https://huggingface.co/MacrOData-CMU.
Tabular Data Adapters: Improving Outlier Detection for Unlabeled Private Data
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））；RPTU Kaiserslautern-Landau, Germany（鲁尔大学（RPTU）科布伦茨-兰道，德国）；Hochschule Bonn-Rhein-Sieg (H-BRS)（波恩-莱茵-锡格应用技术大学（H-BRS））
Fin-Fed-OD: Federated Outlier Detection on Financial Tabular Data
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；RPTU Kaiserslautern-Landau（凯撒斯劳滕-兰道工业大学）；Bonn-Rhein-Sieg University of Applied Sciences（波恩-莱茵-锡格应用科学大学）
TaskFusion: Continual Anomaly Detection for Heterogeneous Tabular Data
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））；RPTU Kaiserslautern-Landau（凯泽斯劳滕-兰道大学）；Hochschule Bonn-Rhein-Sieg (H-BRS)（波恩-莱茵-锡格应用技术大学）
Pretrained deep models outperform GBDTs in Learning-To-Rank under label scarcity
Department of Electrical and Computer Engineering（电气与计算机工程系）；Carnegie Mellon University（卡内基梅隆大学）；Amazon（亚马逊）；Department of Computer Science（计算机科学系）；University of Texas at Austin（德克萨斯大学奥斯汀分校）
Crafting Imperceptible On-Manifold Adversarial Attacks for Tabular Data
School of Information Systems, Queensland University of Technology（信息系统学院，昆士兰技术大学）；Research Centre for Information Systems Engineering (LIRIS), KU Leuven（信息系统工程研究中心（LIRIS），鲁汶大学）；Data Science Institute, University of Technology（数据科学研究院，技术大学）；INESC-ID/Instituto Superior Técnico, University of Lisboa（INESC-ID/理工学院，里斯本大学）
Do Ensembling and Meta-Learning Improve Outlier Detection in Randomized Controlled Trials?
Centre for Data Science and Digital Health, Hamilton Health Sciences（汉密尔顿健康科学中心数据科学与数字健康中心）；Department of Statistical Sciences, University of Toronto（多伦多大学统计科学系）；Institute for Health Policy, Management and Evaluation, University of Toronto（多伦多大学卫生政策、管理与评估研究所）；Department of Medicine, McMaster University（麦克马斯特大学医学系）；Population Health Research Institute（人口健康研究所）
FoMo X: Modular Explainability Signals for Outlier Detection Foundation Models
TU Dortmund University, Dortmund, Germany（图卢兹大学（德累斯顿））；Research Center Trustworthy Data Science and Security, University Alliance Ruhr, Dortmund, Germany（可信数据科学与安全研究中心，鲁尔大学联盟，德累斯顿，德国）
Advancing Tabular Stroke Modelling Through a Novel Hybrid Architecture and Feature-Selection Synergy
Department of Computer Science and Engineering, Leading University, Sylhet 3112, Bangladesh（计算机科学与工程系，领先大学，锡尔het 3112，孟加拉国）；DeepNet Research and Development Lab, Sylhet 3100, Bangladesh（深网研究与发展实验室，锡尔het 3100，孟加拉国）
