pu-learning

Non-negative Positive-Unlabeled (nnPU) and unbiased Positive-Unlabeled (uPU) learning reproductive code on MNIST and CIFAR10

[ICML2020] "Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training" by Xuxi Chen, Wuyang Chen, Tianlong Chen, Ye Yuan, Chen Gong, Kewei Chen, Zhangyang Wang

Code and results accompanying our paper titled Mixture Proportion Estimation and PU Learning: A Modern Approach at Neurips 2021 (Spotlight)

A collection of notebooks that implement algorithms introduced in "Learning from positive and unlabeled data: a survey"

[MICCAI 2022] ShapePU: A New PU Learning Framework Regularized by Global Consistency for Scribble Supervised Cardiac Segmentation

uPU, nnPU and PN learning with Extra Trees classifier.

Official Tensorflow implementation for Deep Generative Positive-Unlabeled Learning under Selection Bias (VAE-PU) in CIKM 2020.

Source code & appendices accompanying the AAAI2022 paper "Unifying Knowledge Base Completion with PU Learning to Mitigate the Observation Bias"

A PU-learning tool on spark

Software implementation of the manuscript "A Two-step Anomaly detection Based Method for PU Classification in Imbalanced Data Sets".

Code to generate SenSet, a novel human lung senescence cell gene signature.

Implementation of the paper: Towards Improved Illicit Node Detection with Positive-Unlabelled Learning

PU Hellinger Trees is a technique for positive and unlabeled imbalanced data.

Mastercard Data Quest: detecting hidden micro-businesses among consumer cardholders with PU-learning and LightGBM on transaction data.

Semi-supervised learning using positive and unlabeled learning for hypertension risk prediction.

National Technological Olympiad AI 25/26 Finals solution. Recovering lost implicit feedback in a Recommender System using a Two-Stage pipeline and PU-learning. Score: 0,1442

ML pipeline that scores companies against an Ideal Customer Profile using firmographic signals: industry, headcount, seniority, and job function.

Finding fraud leads in 11.4M public PPP loans, validated against real DOJ prosecutions — honest about what works and what doesn't.

Project - Semi-supervised learning Anomaly detection in Multivariate Timeseries

Sex-stratified computational pipeline for ALS drug target discovery integrating iPSC transcriptomic reversal, ChEMBL pharmacological annotation, and multi-model machine learning prioritisation.