arXiv Machine Learning By Xueying Ding, Haomin Wen, Simon Kl\"uttermann, Leman Akoglu

From Zero to Hero: Advancing Zero-Shot Foundation Models for Tabular Outlier Detection

Read the original on arXiv Machine Learning →

arXiv:2602. 03018v2 Announce Type: replace Abstract: Outlier detection (OD) is widely used in practice; but its effective deployment on new tasks is hindered by lack of labeled outliers, which makes algorithm and hyperparameter selection notoriously hard.

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arXiv Machine Learning
Jun 29

RANSAC Scoring Done Right

arXiv:2606. 27385v1 Announce Type: new Abstract: The most widely used RANSAC variants score candidate models by counting inliers or summing per-point scores that saturate beyond a residual threshold.

By James Pritts, Felix Seegr\"aber, Kevin K\"oser
arXiv Machine Learning
Sep 11

Optimizing Three Critical Factors for Practical and Effective OOD Detection Fine-Tuning

The paper introduces a framework for out-of-distribution (OOD) detection that addresses the trade‑off between detection performance and classification accuracy caused by fine‑tuning with auxiliary outlier data. It optimizes three factors—model reminder, data sampling, and representation learning—by proposing Self‑Knowledge Distillation to preserve accuracy, Semi‑hard Outlier Sampling to enhance detection with minimal data, and Outlier‑aware Supervised Contrastive Learning to improve ID‑OOD separability. The combined approach yields cumulative gains, outperforming existing methods on diverse benchmarks, especially in long‑tailed scenarios, and offers a robust baseline for real‑world OOD detection.

By Hyunjun Choi, JaeHo Chung, Hawook Jeong
arXiv Machine Learning
Jul 21

AOE: Exhaustive Out-of-Distribution Detection via Recalibrating Outlier Labels

arXiv:2605. 28021v2 Announce Type: replace Abstract: Out-of-distribution (OOD) detection is essential for deploying machine learning models in open-world and safety-critical scenarios, where test inputs may deviate from the training distribution and overconfident predictions on unknown samples can lead to unreliable decisions.

By Fengqiang Wan, Qing-Yuan Jiang, Fu Shen, Yang Yang