Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Rethinking Class-Prior Estimation for Positive-Unlabeled Learning
Authors: Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Gang Niu, Masashi Sugiyama, Dacheng Tao
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We run experiments on 2 synthetic datasets and 9 real word datasets |
| Researcher Affiliation | Collaboration | 1The University of Sydney 2Hong Kong Baptist University 3The University of Melbourne 4RIKEN AIP 5The University of Tokyo 6JD Explore Academy, China |
| Pseudocode | Yes | Algorithm 1 Re CPE |
| Open Source Code | Yes | We have also included an anonymous source code in our supplementary material. |
| Open Datasets | Yes | The real-world datasets are downloaded from the UCL machine learning database. Multi-class datasets are used as binary datasets by either grouping or ignoring classes. |
| Dataset Splits | Yes | We sample the validation set with 20% of the training data size. |
| Hardware Specification | No | The paper mentions training a neural network but does not specify any hardware details such as GPU/CPU models or specific computing resources used for the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names like PyTorch or TensorFlow with their versions). |
| Experiment Setup | Yes | For all experiments, we employ a neural network with 2 hidden layers. Each hidden layer contains 50 hidden units. The batch normalization (Ioffe & Szegedy, 2015) is also employed. The stochastic gradient descent optimizer is used with the batch size 50. The network is trained for 350 epochs with a learning rate 0.01 and momentum 0. The weight decay is set to 1e 5. |