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..
Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification
Authors: Nan Lu, Shida Lei, Gang Niu, Issei Sato, Masashi Sugiyama
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through experiments, we demonstrate the superiority of our proposed method over state-of-the-art methods. |
| Researcher Affiliation | Academia | 1The University of Tokyo, Tokyo, Japan 2RIKEN, Tokyo, Japan. |
| Pseudocode | Yes | Algorithm 1 Um-SSC based on stochastic optimization |
| Open Source Code | Yes | Our implementation of Um-SSC is available at https://github.com/leishida/Um-Classification. |
| Open Datasets | Yes | Datasets We train on widely adopted benchmarks MNIST, Fashion-MNIST, Kuzushiji-MNIST, and CIFAR-10. |
| Dataset Splits | No | The paper mentions 'training data' and 'test phase' but does not explicitly provide details for a distinct validation set or its split. |
| Hardware Specification | No | The paper does not specify the hardware used for running the experiments (e.g., GPU models, CPU types). |
| Software Dependencies | No | The paper mentions using 'Adam (Kingma & Ba, 2015) with the cross-entropy loss for optimization' but does not specify version numbers for Adam, the specific deep learning framework (e.g., PyTorch, TensorFlow), or other software dependencies. |
| Experiment Setup | Yes | We train 300 epochs for all the experiments, and the classification error rates at the test phase are reported. All the experiments are repeated 3 times and the mean values with standard deviations are recorded for each method. |