Alternatives and similar repositories for arbitrary_pu:

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

• cyber-meow / PUbiasedN
  PyTorch implementation for the paper Classification from Positive, Unlabeled and Biased Negative Data.
  ☆19Updated last year
• Stomach-ache / PU-Learning
  This repo lists some researches and applications in PU learning.
  ☆13Updated 5 years ago
• MasaKat0 / PUlearning
  Code for Positive-Unlabeled learning.
  ☆35Updated 2 years ago
• cimeister / pu-learning
  Pytorch implementation of risk estimators for unbiased and non-negative positive-unlabeled learning
  ☆86Updated 6 months 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
• acmi-lab / PU_learning
  Code and results accompanying our paper titled Mixture Proportion Estimation and PU Learning: A Modern Approach at Neurips 2021 (Spotligh…
  ☆44Updated last year
• ThyrixYang / gdfm_nips22
  code of Generalized Delayed Feedback Model with Post-Click Information in Recommender Systems, NeurIPS 2022
  ☆10Updated 2 years ago
• RyanWangZf / Influence_Subsampling
  Official Implementation of Unweighted Data Subsampling via Influence Function - AAAI 2020
  ☆65Updated 3 years ago
• LARS-research / S2E
  Q. Yao, H. Yang, B. Han, G. Niu, J. Kwok. Searching to Exploit Memorization Effect in Learning from Noisy Labels. ICML 2020
  ☆22Updated 4 years ago
• ninghaohello / Interpretation2Adversary
  Adversarial learning by utilizing model interpretation
  ☆10Updated 6 years ago
• JunwenBai / MPVAE
  MPVAE: Multivariate Probit Variational AutoEncoder for Multi-Label Classification
  ☆31Updated 5 months ago
• v-mipeng / ClassImbalanceLearning
  This is the implementation code for the paper "Trainable Undersampling for Class-Imbalance Learning" published in AAAI2019
  ☆16Updated 6 years ago
• lethaiq / GRACE_KDD20
  GRACE: Generating Concise and Informative Contrastive Sample to Explain Neural Network Model’s Prediction. Thai Le, Suhang Wang, Dongwon …
  ☆21Updated 4 years ago
• suamin / LowFER
  LowFER: Low-rank Bilinear Pooling for Link Prediction (ICML 2020)
  ☆13Updated 2 years ago
• DEEP-PolyU / GraphRNA_KDD19
  Graph Recurrent Networks with Attributed Random Walks
  ☆28Updated last year
• wangjiachun0426 / StackRec
  ☆24Updated 3 years ago
• pd90506 / AMCF
  Explainable recommendation via interpretable feature mapping
  ☆18Updated 4 years ago
• JeanKaddour / SIN
  Causal Effect Inference for Structured Treatments (SIN) (NeurIPS 2021)
  ☆42Updated 2 years ago
• lehaifeng / Awesome-Adversarial-Sample-on-Graph
  ☆15Updated 6 years ago
• karenyang / GCN-VAE
  project GCN-VAE for knowledge graphs
  ☆16Updated 4 years ago
• DeqingYang / ACAM-model
  It is deep recommendation model with attribute-level co-attention, which has been accepted as a short paper in SIGIR2020.
  ☆9Updated 4 years ago
• GarrettLee / nnpu_tf
  Code for reproducing experiments on MNIST and CIFAR-10 in paper "Positive-Unlabeled Learning with Non-Negative Risk Estimator".
  ☆21Updated 2 years ago
• bluer555 / KernelGCN
  Codes for NIPS 2019 Paper: Rethinking Kernel Methods for Node Representation Learning on Graphs
  ☆34Updated 5 years ago
• CheriseZhu / GSSNN
  The implementation of our AAAI 2020 paper "GSSNN: Graph Smoothing Splines Neural Network".
  ☆20Updated 4 years ago
• pl8787 / SetRank
  Implementation of SetRank in SIGIR 2020
  ☆50Updated 4 years ago
• vikasverma1077 / GraphMix
  Code for reproducing results in GraphMix paper
  ☆72Updated 2 years ago
• matenure / OTCoarsening
  Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport
  ☆36Updated 4 years ago
• ML-KULeuven / SAR-PU
  Beyond the Selected Completely At Random Assumption for Learning from Positive and Unlabeled Data
  ☆33Updated 8 months ago
• nikhil-dce / SBM-meet-GNN
  Implementation of SBM-meet-GNN
  ☆22Updated 5 years ago