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..
Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical
Authors: Wei Wang, Takashi Ishida, Yu-Jie Zhang, Gang Niu, Masashi Sugiyama
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results on both synthetic and real-world benchmark datasets validate the superiority of our proposed approach over state-of-the-art methods. |
| Researcher Affiliation | Academia | 1The University of Tokyo 2RIKEN. |
| Pseudocode | Yes | Algorithm 1 SCARCE |
| Open Source Code | Yes | Our implementation of SCARCE is available at https://github.com/wwangwitsel/SCARCE. |
| Open Datasets | Yes | We conducted experiments on synthetic benchmark datasets, including MNIST (Le Cun et al., 1998), Kuzushiji-MNIST (Clanuwat et al., 2018), Fashion-MNIST (Xiao et al., 2017), and CIFAR-10 (Krizhevsky & Hinton, 2009). |
| Dataset Splits | Yes | The training curves and test curves of the method that works by minimizing the URE in Eq. (9) are shown in Figure 1. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models used for running the experiments. |
| Software Dependencies | No | All the methods were implemented in Py Torch (Paszke et al., 2019). We used the Adam optimizer (Kingma & Ba, 2015). |
| Experiment Setup | Yes | The learning rate and batch size were fixed to 1e-3 and 256 for all the datasets, respectively. The weight decay was 1e-3 for CIFAR-10 and 1e-5 for the other three datasets. The number of epochs was set to 200, and we recorded the mean accuracy in the last ten epochs. |