Why Does Counterintuitive Likelihood Behavior Rarely Emerge in Tabular Anomaly Detection?
Department of Industrial Engineering, University of Yonsei, Republic of Korea, Seoul（延世大学工业工程系）；H. Milton Stewart School of Industrial and Systems Engineering (ISyE), Georgia Institute of Technology, USA, GA（佐治亚理工学院H.米尔顿·斯图尔特工业与系统工程学院）
Deep generative models with tractable likelihoods, exemplified by normalizing flows, offer an effective basis for anomaly detection through likelihood-based scoring. We demonstrate that, unlike in the image domain where deep generative models frequently assign higher likelihoods to anomalous data, such counterintuitive behavior occurs rarely in tabular settings. We first introduce a domain-agnostic formulation that enables consistent detection and evaluation of the counterintuitive phenomenon, addressing the lack of a precise definition in prior work. Through extensive experiments on 47 tabular datasets benchmarked against 13 baseline models and on 10 CV/NLP embedding datasets from ADBench, we show that the phenomenon is consistently rare in general tabular data and that normalizing flow likelihood achieves competitive anomaly detection performance. We further provide theoretical and empirical analyses attributing this disparity to differences in dimensionality and feature correlation between tabular and image data. Therefore, our results indicate that likelihood-based detection with normalizing flows is a practical approach for tabular anomaly detection.
Towards Benchmarking Foundation Models for Tabular Data With Text
Department of Computer Science, University of Freiburg, Freiburg, Germany（弗赖堡大学计算机科学系）；Prior Labs, Freiburg, Germany（Prior实验室）；ELLIS Institute, Tubingen, Germany（图宾根ELLIS研究所）
Dependency-aware synthetic tabular data generation
Institute of Computer Science, University of Rostock, Germany（罗斯托克大学计算机科学研究所）；Leibniz-Institute for Food Systems Biology, Technical University of Munich, Freising, Germany（马克斯·普朗克食品系统生物学研究所，慕尼黑技术大学）；Stellenbosch Institute for Advanced Study, South Africa（斯坦伦博斯高级研究学院，南非）；School of Data Science, Indian Institute of Science Education（印度科学教育与研究学院数据科学学院）
TabStruct: Measuring Structural Fidelity of Tabular Data
Department of Computer Science and Technology, University of Cambridge（计算机科学与技术系，剑桥大学）；Télécom Paris, Institut Polytechnique de Paris（巴黎理工 Télécom Paris）
Fed-ReMasker: Federated Tabular Imputation under Feature-Level Missingness
ARTORG Center for Biomedical Engineering Research University of Bern（伯尔尼大学生物医学工程研究中心）；Graduate School for Cellular and Biomedical Sciences University of Bern（伯尔尼大学细胞与生物医学科学研究生院）
Anomaly Detection via Mean Shift Density Enhancement
School of Data Science, Indian Institute of Science Education and Research（数据科学学院，印度科学教育与研究学院）；Institute of Computer Science, University of Rostock（计算机科学研究所，罗斯托克大学）；Leibniz-Institute for Food Systems Biology, Technical University of Munich（食品系统生物学莱比锡研究所，慕尼黑技术大学）；Stellenbosch Institute of Advanced Studies (STIAS)（斯托尔波茨堡高级研究 institute (STIAS)）
One Pipeline, Many Transformers: Pattern-Specific Imputation Specialists for Tabular Missing Data
Industrial Engineering \& Operations Research, Columbia University, New York, USA；Department of Statistics, Columbia University, New York, USA；Operations Research \& Information Engineering, Cornell Tech, New York, USA
Knowledge-Data Dually Driven Paradigm for Accurate Landslide Susceptibility Prediction under Data-Scarce Conditions Using Geomorphic Priors and Tabular Foundation Model
School of Engineering and Technology, China University of Geosciences (Beijing)（工程与技术学院，中国地质大学（北京））；School of Geological Engineering and Geomatics, Chang'an University（地质工程与测绘学院，长安大学）
Resilient Vision-Tabular Multimodal Learning under Modality Missingness
Research Unit of Artificial Intelligence and Computer Systems, Department of Engineering, Università Campus Bio-Medico di Roma（人工智能与计算机系统研究单位，工程系，罗马生物医学大学）；Department of Diagnostics and Intervention, Radiation Physics, Biomedical Engineering, Umeå University（诊断与干预系，辐射物理，生物医学工程，乌梅大学）
A Multi-Scale Graph Learning Framework with Temporal Consistency Constraints for Financial Fraud Detection in Transaction Networks under Non-Stationary Conditions
