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..
A Multi-objective / Multi-task Learning Framework Induced by Pareto Stationarity
Authors: Michinari Momma, Chaosheng Dong, Jia Liu
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate not only the method achieves competitive performance with existing methods, but also it allows us to achieve the performance from different forms of preferences. |
| Researcher Affiliation | Collaboration | 1Amazon.com Inc. 2The Ohio State University. |
| Pseudocode | Yes | Algorithm 1 XWC-MGDA |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code for the described methodology or links to a code repository. |
| Open Datasets | Yes | For image classification, we use three datasets: (1) Multi MNIST (Sabour et al., 2017), (2) Multi-Fashion (Xiao et al., 2017), and (3) Multi-Fashion+MNIST (Lin et al., 2019b). |
| Dataset Splits | No | In each dataset, there are 120,000 samples in the training set and 20,000 samples in the test set. No specific information about a validation set split is provided. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running experiments. |
| Software Dependencies | No | The paper mentions models like 'Le Net' and 'fully connected feed-forward neural network (FNN)' but does not list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | No | The paper mentions architectural choices (Le Net, 4-layer FNN) and loss functions (MSE, SBCE), and using a random seed, but does not provide specific hyperparameter values such as learning rate, batch size, or optimizer settings for the experiments. |