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 Multi-Level Task Groups in Multi-Task Learning
Authors: Lei Han, Yu Zhang
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experiment our approach on both synthetic and real-world datasets, showing competitive performance over state-of-the-art MTL methods. |
| Researcher Affiliation | Academia | Lei Han1 and Yu Zhang1,2 1Department of Computer Science, Hong Kong Baptist University, Hong Kong 2The Institute of Research and Continuing Education, Hong Kong Baptist University (Shenzhen) |
| Pseudocode | Yes | Algorithm 1 The Bottom-Up Iterative Scheme for Problem (3). |
| Open Source Code | No | The paper does not contain any explicit statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | We report results on microarray data (Wille et al. 2004). |
| Dataset Splits | Yes | We perform 10 random splits, each of which uses R%, (80-R)%, and 20% samples for training, testing and validation separately with R as the training ratio. |
| Hardware Specification | No | No specific hardware details (like GPU or CPU models, or memory specifications) used for running experiments are provided in the paper. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names like PyTorch or scikit-learn with their versions). |
| Experiment Setup | Yes | We set ε0 = 0.6 in the experiments, which shows a better discrimination. |