Alternatives and similar repositories for PU_learning:

Users that are interested in PU_learning are comparing it to the libraries listed below

• ZaydH / arbitrary_pu
  NeurIPS'20 Paper: "Learning from Positive and Unlabeled Data with Arbitrary Positive Shift"
  ☆17Updated last year
• HC-Feynman / vpu
  A PyTorch implementation of the Variational approach for PU learning
  ☆29Updated 4 years ago
• Ray-rui / Dist-PU-Positive-Unlabeled-Learning-from-a-Label-Distribution-Perspective
  PyTorch implementation of Dist-PU (CVPR 2022)
  ☆25Updated 2 years ago
• cimeister / pu-learning
  Pytorch implementation of risk estimators for unbiased and non-negative positive-unlabeled learning
  ☆87Updated 8 months ago
• ahujak / IB-IRM
  ☆30Updated 3 years ago
• JonathanCrabbe / Simplex
  This repository contains the implementation of SimplEx, a method to explain the latent representations of black-box models with the help …
  ☆24Updated 2 years ago
• mlfoundations / tableshift
  A benchmark for distribution shift in tabular data
  ☆52Updated 11 months ago
• JeanKaddour / SIN
  Causal Effect Inference for Structured Treatments (SIN) (NeurIPS 2021)
  ☆42Updated 3 years ago
• huaxiuyao / MetaMix
  MetaMix for ICML 2021
  ☆27Updated 3 years ago
• MLforHealth / ClinicalDG
  An Empirical Framework for Domain Generalization In Clinical Settings
  ☆30Updated 3 years ago
• changliu00 / causal-semantic-generative-model
  Codes for Causal Semantic Generative model (CSG), the model proposed in "Learning Causal Semantic Representation for Out-of-Distribution …
  ☆73Updated 3 years ago
• RyanWangZf / Influence_Subsampling
  Official Implementation of Unweighted Data Subsampling via Influence Function - AAAI 2020
  ☆65Updated 4 years ago
• capybaralet / REx_code_release
  ☆64Updated 4 years ago
• ecreager / eiil
  Code for Environment Inference for Invariant Learning (ICML 2021 Paper)
  ☆50Updated 3 years ago
• siplab-gt / generative-causal-explanations
  Code for "Generative causal explanations of black-box classifiers"
  ☆34Updated 4 years ago
• ritaranx / NeST
  [AAAI 2023] This is the code for our paper `Neighborhood-Regularized Self-Training for Learning with Few Labels'.
  ☆12Updated 2 years ago
• Luodian / IIB
  ☆16Updated 3 years ago
• VITA-Group / Self-PU
  [ICML2020] "Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training" by Xuxi Chen, Wuyang Chen, Tianlong Chen, Ye Yuan, Chen Gon…
  ☆68Updated 3 years ago
• maksym33 / FeatureImportanceDL
  ☆43Updated 2 years ago
• JointEntropy / awesome-ml-pu-learning
  A curated list of resources dedicated to Positive Unlabeled(PU) learning ML methods.
  ☆36Updated 2 years ago
• cyber-meow / PUbiasedN
  PyTorch implementation for the paper Classification from Positive, Unlabeled and Biased Negative Data.
  ☆19Updated last year
• Out-of-Distribution-Generalization / Out-of-Distribution-Generalization.github.io
  ☆26Updated 2 years ago
• code-terminator / invariant_rationalization
  Tensorflow implementation of Invariant Rationalization
  ☆49Updated 2 years ago
• kakaobrain / irm-empirical-study
  An Empirical Study of Invariant Risk Minimization
  ☆27Updated 4 years ago
• TongtongFANG / DIW
  Code for the paper "Rethinking Importance Weighting for Deep Learning under Distribution Shift".
  ☆30Updated 4 years ago
• ML-KULeuven / SAR-PU
  Beyond the Selected Completely At Random Assumption for Learning from Positive and Unlabeled Data
  ☆33Updated 10 months ago
• Stomach-ache / PU-Learning
  This repo lists some researches and applications in PU learning.
  ☆12Updated 5 years ago
• microsoft / MLC
  Meta Label Correction for Noisy Label Learning
  ☆85Updated 2 years ago
• a5507203 / Instance-dependent-Label-noise-Learning-under-a-Structural-Causal-Model
  ☆16Updated last year
• YyzHarry / SubpopBench
  [ICML 2023] Change is Hard: A Closer Look at Subpopulation Shift
  ☆108Updated last year