Cover Image

Kurtosis-Guided Denoising Score Matching for Tabular Anomaly Detection

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Livernoche, Victor, Zan, Jie, Rabbany, Reihaneh
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

On Diffusion Modeling for Anomaly Detection
by: Livernoche, Victor, et al.
Published: (2023)

Higher-Order Transformers With Kronecker-Structured Attention
by: Omranpour, Soroush, et al.
Published: (2024)

Towards Detecting Contextual Real-Time Toxicity for In-Game Chat
by: Yang, Zachary, et al.
Published: (2023)

GPS-SSL: Guided Positive Sampling to Inject Prior Into Self-Supervised Learning
by: Feizi, Aarash, et al.
Published: (2024)

OpenFake: An Open Dataset and Platform Toward Real-World Deepfake Detection
by: Livernoche, Victor, et al.
Published: (2025)

Manifold Aware Denoising Score Matching (MAD)
by: Levy-Jurgenson, Alona, et al.
Published: (2026)

Unsupervised Anomaly Detection for Tabular Data Using Noise Evaluation
by: Dai, Wei, et al.
Published: (2024)

Investigating Mask-aware Prototype Learning for Tabular Anomaly Detection
by: Lu, Ruiying, et al.
Published: (2025)

Anomaly Detection of Tabular Data Using LLMs
by: Li, Aodong, et al.
Published: (2024)

Disentangling Tabular Data Towards Better One-Class Anomaly Detection
by: Ye, Jianan, et al.
Published: (2024)

RL Fine-Tuning Heals OOD Forgetting in SFT
by: Jin, Hangzhan, et al.
Published: (2025)

Multi-Agent Debate: A Unified Agentic Framework for Tabular Anomaly Detection
by: Wang, Pinqiao, et al.
Published: (2026)

A 1/R Law for Kurtosis Contrast in Balanced Mixtures
by: Bi, Yuda, et al.
Published: (2026)

CausalTAD: Injecting Causal Knowledge into Large Language Models for Tabular Anomaly Detection
by: Wang, Ruiqi, et al.
Published: (2026)

ReTabAD: A Benchmark for Restoring Semantic Context in Tabular Anomaly Detection
by: Yoon, Sanghyu, et al.
Published: (2025)

Back to Repair: A Minimal Denoising Network for Time Series Anomaly Detection
by: Özer, Kadir-Kaan, et al.
Published: (2026)

DenoMAE2.0: Improving Denoising Masked Autoencoders by Classifying Local Patches
by: Faysal, Atik, et al.
Published: (2025)

Why the Counterintuitive Phenomenon of Likelihood Rarely Appears in Tabular Anomaly Detection with Deep Generative Models?
by: Kim, Donghwan, et al.
Published: (2026)

When Normality Shifts: Risk-Aware Test-Time Adaptation for Unsupervised Tabular Anomaly Detection
by: Huang, Wei, et al.
Published: (2026)

FedAD-Bench: A Unified Benchmark for Federated Unsupervised Anomaly Detection in Tabular Data
by: Anwar, Ahmed, et al.
Published: (2024)

Explainable Heterogeneous Anomaly Detection in Financial Networks via Adaptive Expert Routing
by: Li, Zan, et al.
Published: (2025)

PairBench: Are Vision-Language Models Reliable at Comparing What They See?
by: Feizi, Aarash, et al.
Published: (2025)

Extending Tabular Denoising Diffusion Probabilistic Models for Time-Series Data Generation
by: Dobhal, Umang, et al.
Published: (2026)

Qsco: A Quantum Scoring Module for Open-set Supervised Anomaly Detection
by: Peng, Yifeng, et al.
Published: (2024)

DiffImpute: Tabular Data Imputation With Denoising Diffusion Probabilistic Model
by: Wen, Yizhu, et al.
Published: (2024)

Diffusion and Flow Matching Models for Tabular Data: A Survey
by: Li, Zhong, et al.
Published: (2025)

Scalable, Explainable and Provably Robust Anomaly Detection with One-Step Flow Matching
by: Li, Zhong, et al.
Published: (2025)

Improving Interpretability of Scores in Anomaly Detection Based on Gaussian-Bernoulli Restricted Boltzmann Machine
by: Sekimoto, Kaiji, et al.
Published: (2024)

TGM: a Modular and Efficient Library for Machine Learning on Temporal Graphs
by: Chmura, Jacob, et al.
Published: (2025)

Active Tabular Augmentation via Policy-Guided Diffusion Inpainting
by: Zhang, Zheyu, et al.
Published: (2026)

Beyond Augmentation: Score-Guided Pathological Prior for EEG-based Depression Detection
by: Chen, Xiaojing, et al.
Published: (2026)

Large Language Model Guided Knowledge Distillation for Time Series Anomaly Detection
by: Liu, Chen, et al.
Published: (2024)

Distributional Regression with Tabular Foundation Models: Evaluating Probabilistic Predictions via Proper Scoring Rules
by: Landsgesell, Jonas, et al.
Published: (2026)

Weak Supervision for Real World Graphs
by: Nair, Pratheeksha, et al.
Published: (2025)

M$^2$AD: Multi-Sensor Multi-System Anomaly Detection through Global Scoring and Calibrated Thresholding
by: Alnegheimish, Sarah, et al.
Published: (2025)

Kernel Regression in Structured Non-IID Settings: Theory and Implications for Denoising Score Learning
by: Zhang, Dechen, et al.
Published: (2025)

Detecting Anomalies in Dynamic Graphs via Memory enhanced Normality
by: Liu, Jie, et al.
Published: (2024)

Are Anomaly Scores Telling the Whole Story? A Benchmark for Multilevel Anomaly Detection
by: Cao, Tri, et al.
Published: (2024)

Online Detection of Anomalies in Temporal Knowledge Graphs with Interpretability
by: Zhang, Jiasheng, et al.
Published: (2024)

A Survey on Diffusion Models for Anomaly Detection
by: Liu, Jing, et al.
Published: (2025)