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..
Self-Paced Multi-Task Learning
Authors: Changsheng Li, Junchi Yan, Fan Wei, Weishan Dong, Qingshan Liu, Hongyuan Zha
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on the toy and real-world datasets demonstrate the effectiveness of the proposed approach, compared to the state-of-the-arts. |
| Researcher Affiliation | Collaboration | Changsheng Li12, Junchi Yan12 , Fan Wei,3 Weishan Dong,2 Qingshan Liu,4 Hongyuan Zha51 1East China Normal University 2IBM Research China 3Stanford University 4Nanjing University of Info. Science & Tech 5Georgia Institute of Technology |
| Pseudocode | Yes | Algorithm 1 Self-Paced Multi-Task Learning (SPMTL) |
| Open Source Code | No | The paper does not contain any statement or link indicating that source code for their methodology is provided or publicly available. |
| Open Datasets | Yes | OHSUMED (Hersh et al. 1994) and Isolet1. The first one is an ordinal regression dataset... The second dataset is collected from 150 speakers... 1http://www.cad.zju.edu.cn/home/dengcai/Data/MLData.html |
| Dataset Splits | No | The paper mentions "randomly select the training instances from each task with different training ratios (5%, 10% and 15%) and use the rest of instances to form the testing set," but does not explicitly mention a distinct validation set or its split. |
| Hardware Specification | No | The paper does not provide specific hardware details such as CPU/GPU models, memory, or specific computing environments used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., programming languages, libraries, frameworks). |
| Experiment Setup | Yes | The regularization parameter α in (3) is used to control the complexity of the basis tasks. We find α = 100 works well on all the three datasets, and thus fix it to 100 throughout the experiments. The parameter β is tuned in the space [0.001, 0.01, 0.1, 1, 10, 100]. The parameters λ and γ influence how many tasks will be selected for training. Thus we initially set more than 20% tasks selected in the experiment. To determine the corresponding λ and γ, we adopt the grid search strategy based on the principle that larger λ and smaller γ can make more weights to be larger. After initialization, we increase λ and decrease γ to gradually involve hard tasks and instances at each iteration. |