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..
Model Fusion for Personalized Learning
Authors: Thanh Chi Lam, Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4. Theoretical Analysis; 5. Experiments; The evaluation shows that the personalized model ο¬ne-tuned with limited data performs competitively to a model built on extensive data. |
| Researcher Affiliation | Collaboration | 1National University of Singapore 2AWS AI Labs, Amazon 3Massachusetts Institute of Technology. |
| Pseudocode | No | The paper describes its methods in text and does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code: https://github.com/zevergreenz/model_fusion_for_personalized_learning |
| Open Datasets | Yes | 5.2. MNIST Dataset (Le Cun et al., 2010), Le Cun, Y., Cortes, C., and Burges, C. Mnist handwritten digit database. ATT Labs [Online]. Available: http://yann. lecun. com/exdb/mnist, 2, 2010. and 5.3. Movie-Len Dataset (Harper & Konstan, 2015), Harper, F. M. and Konstan, J. A. The Movie Lens datasets: History and context. ACM Transactions on Interactive Intelligent Systems (TIIS), 5(4):1 19, 2015. |
| Dataset Splits | No | The paper mentions training and testing sets for its experiments but does not explicitly describe a validation dataset split or how it was used. |
| Hardware Specification | No | The paper describes the experimental setup and results but does not provide specific hardware details such as GPU/CPU models or memory specifications used for running experiments. |
| Software Dependencies | No | The paper mentions that code is available but does not specify any software dependencies with version numbers (e.g., specific Python, PyTorch, or TensorFlow versions) that would be needed for replication. |
| Experiment Setup | No | The paper describes model architectures (e.g., '1-layer neural net with 100 hidden neurons, [1-100-1], with Re LU activation') and the few-shot learning setup, but does not provide specific hyperparameter values like learning rates, batch sizes, or optimizer settings. |