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..
Instance-Dependent Partial Label Learning
Authors: Ning Xu, Congyu Qiao, Xin Geng, Min-Ling Zhang
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on benchmark and real-world datasets validate the effectiveness of the proposed method. |
| Researcher Affiliation | Academia | Ning Xu, Congyu Qiao, Xin Geng , and Min-Ling Zhang School of Computer Science and Engineering, Southeast University, Nanjing 210096, China MOE Key Laboratory of Computer Network and Information Integration, Ministry of Education, China EMAIL |
| Pseudocode | Yes | Algorithm 1 VALEN Algorithm |
| Open Source Code | Yes | Source code is available at https://github.com/palm-ml/valen. |
| Open Datasets | Yes | We adopt four widely used benchmark datasets including MNIST [22], Fashion-MNIST [32], Kuzushiji-MNIST [6], and CIFAR-10 [21], and five datasets from the UCI Machine Learning Repository [1], including Yeast, Texture, Dermatology, Synthetic Control, and 20Newgroups. [...] The datasets corrupted by the instance-dependent generating procedure are available at https://drive.google.com/drive/folders/1J_68EqOrLN6tA56RcyTgcr1komJB31Y1?usp=sharing. |
| Dataset Splits | Yes | We run 5 trials on the four benchmark datasets and perform five-fold cross-validation on UCI datasets and real-world PLL datasets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | We implement the comparing methods with Py Torch. The paper mentions PyTorch but does not specify a version number or other software dependencies with versions. |
| Experiment Setup | Yes | Specifically, the 32-layer Res Net is trained on CIFAR-10 in which the learning rate, weight decay and mini-batch size are set to 0.05, 10-3 and 256, respectively. The three-layer MLP is trained on MNIST, Fashion-MNIST and Kuzushiji-MNIST where the learning rate, weight decay and mini-batch size are set to 10-2, 10-4 and 256, respectively. The linear model is trained on UCI and real-world PLL datasets where the learning rate, weight decay and mini-batch size are set to 10-2, 10-4 and 100, respectively. The number of epochs is set to 500, in which the first 10 epochs are warm-up training. |