Supplementary material for IJCNN paper "XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning"

E3Outlier: Effective End-to-end Unsupervised Outlier Detection

The official implementation of CVPR2023 paper "DISC: Learning from Noisy Labels via Dynamic Instance-Specific Selection and Correction"

Official repository for Is your noise correction noisy? PLS: Robustness to label noise with two stage detection WACV 2023

An implementation of IDS (Interpretable Decision Sets) algorithm.

PyTorch Lightning implementation of PTaRL: Prototype-based Tabular Representation Learning via Space Calibration

Learning Certifiably Optimal Rule Lists

A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques

A PyTorch implementation of Deep SAD, a deep Semi-supervised Anomaly Detection method.

Anomaly detection related books, papers, videos, and toolboxes

Code for paper: DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Beyond Outlier Detection: LookOut for Pictorial Explanation

ICML'19 How does Disagreement Help Generalization against Label Corruption?

Agree to Disagree: Robust Anomaly Detection with Noisy Labels (SIGMOD 2025)

Official Implementation of Early-Learning Regularization Prevents Memorization of Noisy Labels

The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.

A Benchmark for Joint Data Cleaning and Machine Learning

NeurIPS'18: Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels

Efficient multivariate correlation measure / high contrast subspace miner.

This code package implements Algorithm FRL and Algorithm softFRL described in the paper "An Optimization Approach to Learning Falling Rule Lists" by Chen and Rudin (AISTATS 2018).

Ruleset covering algorithms for transparent machine learning

Public home of pycorels, the python binding to CORELS

Label-Noise Learning with Intrinsically Long-Tailed Data（ICCV2023）

paper "O2U-Net: A Simple Noisy Label Detection Approach for Deep Neural Networks" code