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 from Noisy Labels with No Change to the Training Process
Authors: Mingyuan Zhang, Jane Lee, Shivani Agarwal
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments confirm our theoretical findings. We conducted two sets of experiments to evaluate our noise-corrected plug-in algorithm. |
| Researcher Affiliation | Collaboration | 1Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA 2Twitter, San Francisco, CA, USA (Work done while at the University of Pennsylvania). |
| Pseudocode | Yes | Algorithm 1 Noise-Corrected Plug-in Algorithm; Algorithm 3 Iterative Noise Estimation Heuristic |
| Open Source Code | No | The paper does not explicitly state that the source code for the proposed methodology is openly available or provide a link to it. |
| Open Datasets | Yes | Here we describe experiments on two benchmark data sets, MNIST (Lecun et al., 1998) and CIFAR10 (Krizhevsky & Hinton, 2009) |
| Dataset Splits | No | The paper specifies training and test set sizes for synthetic data but does not explicitly detail a separate validation split or its size/methodology. For real datasets, it states it mimicked Patrini et al.'s settings but does not provide specific split details in this paper. |
| Hardware Specification | No | No specific hardware details (like GPU or CPU models, memory, or cloud instance types) used for running experiments are provided in the paper. |
| Software Dependencies | No | The implementation was in Py Torch (Paszke et al., 2019), and used the Adam W optimizer. No version numbers for PyTorch or Adam W optimizer are provided. |
| Experiment Setup | Yes | The optimizer was run for 50 epochs over the training sample; the learning rate parameter was initially set to 0.01 and was halved at the end of every 5 epochs. |