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..
Predictive Adversarial Learning from Positive and Unlabeled Data
Authors: Wenpeng Hu, Ran Le, Bing Liu, Feng Ji, Jinwen Ma, Dongyan Zhao, Rui Yan7806-7814
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Evaluation using both image and text data shows that PAN outperforms state-of-the-art PU learning methods and also a direct adaptation of GAN for PU learning. |
| Researcher Affiliation | Collaboration | Wenpeng Hu1,*, Ran Le2,*, Bing Liu3, , Feng Ji4, Jinwen Ma1, Dongyan Zhao2, Rui Yan2, 1 Department of Information Science, School of Mathematical Sciences, Peking University 2 Wangxuan Institute of Computer Technology, Peking University 3 Department of Computer Science, University of Illinois at Chicago 4 Alibaba Group EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 PAN training by the minibatch stochastic gradient descent method. |
| Open Source Code | No | The paper does not provide concrete access to source code for the PAN methodology described in this paper. It only references open-source code for baselines. |
| Open Datasets | Yes | YELP: http://www.yelp.com/dataset challenge; RT: http://www.cs.cornell.edu/ people/pabo/movie-review-data/; IMDB: https://www.imdb.com/interfaces/; 20NEWS: http://qwone.com/ jason/20Newsgroups/; MNIST: http://yann.lecun.com/ exdb/mnist/; CIFAR10: http://www.cs.toronto.edu/ kriz/cifar10-python.tar.gz . |
| Dataset Splits | No | The paper describes how training data (Positive P and Unlabeled U) and test data are prepared, but does not explicitly mention a separate validation dataset split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments, only general training configurations. |
| Software Dependencies | No | The paper mentions using 'Adam algorithm' for optimization and 'tensorflow' for a baseline, but does not specify version numbers for programming languages, libraries, or frameworks used in the experiments. |
| Experiment Setup | Yes | Training Details: For a fair comparison, PAN uses the same architecture for classifier C( ) as NNPU. For text, a 2-layer convolutional network (CNN), with 5 * 100 and 3 * 100 convolutions for layers 1 and 2 respectively, and 100 filters for each layer, is used as the classifier C( ) and discriminator D( ). (...) We set λ in Eq. 3 and Eq. 7 to 0.0001, (...) We also balance the impact of positive and unlabeled data for term I in Eq. 3 in training; otherwise the positive examples will be dominated by the unlabeled data. We use 1:1 ratio of positive data and unlabeled data in each mini-batch in training. The network parameters are updated using the Adam algorithm with learning rate 0.0001. For a-GAN, it needs pre-training of D( ). We use the original positive and unlabeled (regarded as negative) data to pre-train D( ) in order to give it the ability to classify positive and unlabeled data. We pre-train D( ) 3 epochs for each dataset. |