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..
Mediated Uncoupled Learning: Learning Functions without Direct Input-output Correspondences
Authors: Ikko Yamane, Junya Honda, Florian Yger, Masashi Sugiyama
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We prove statistical consistency and error bounds of our method and experimentally confirm its practical usefulness. |
| Researcher Affiliation | Academia | 1LAMSADE, CNRS, Université Paris-Dauphine, PSL Research University, 75016 PARIS, FRANCE 2RIKEN AIP, Tokyo, Japan 3Kyoto University, Kyoto, Japan 4The University of Tokyo, Tokyo, Japan. Correspondence to: Ikko Yamane <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Two-Step Regressed Regression (2Step-RR); Algorithm 2 Joint Regressed Regression (Joint-RR) |
| Open Source Code | Yes | The code will be available on https://github.com/i-yamane/mediated_uncoupled_learning. |
| Open Datasets | Yes | MNIST (Le Cun et al., 1994), Fashion-MNIST (Xiao et al., 2017), CIFAR-10, and CIFAR-100 (Krizhevsky et al., 2009). |
| Dataset Splits | Yes | We use 1,000 mediated uncoupled data for training and 10,000 coupled (X, Y )-data for test evaluation. (...) We use randomly sampled 10,000 mediated uncoupled data for training and 10,000 coupled (X, Y )-data for test evaluation. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Py Torch (Paszke et al., 2019)' and 'Adam (Kingma & Ba, 2017)' but does not provide specific version numbers for PyTorch or any other libraries or dependencies used. |
| Experiment Setup | Yes | We train all models with Adam (Kingma & Ba, 2017) for 200 epochs. (...) We use the default values of the implementation provided by Py Torch (Paszke et al., 2019) for all the parameters of Adam: the learning rate is 0.001, and β is (0.9, 0.999). (...) We turn off the weight decay and set the other tuning parameters of Adam as in Py Torch (Paszke et al., 2019): the learning rate is 0.001, the β is (0.9, 0.999). |