Exploiting Task-Feature Co-Clusters in Multi-Task Learning
Authors: Linli Xu, Aiqing Huang, Jianhui Chen, Enhong Chen
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate the proposed approach of multitask learning with task-feature co-clusters (Co CMTL) in comparison with single task learning methods as well as representative multi-task learning algorithms. The experiments are first conducted in a synthetic setting, and then on two real-world data sets. |
| Researcher Affiliation | Collaboration | School of Computer Science and Technology University of Science and Technology of China, Hefei, Anhui 230027, China Yahoo Labs, Sunnyvale, CA 94089, USA |
| Pseudocode | Yes | Algorithm 1 Iterative algorithm for computing Qr |
| Open Source Code | No | The paper mentions "All algorithms are implemented with MATLAB." but does not provide any concrete access to the source code for the methodology described, nor does it state that the code is open-source or available. |
| Open Datasets | Yes | The School data consists of the exam scores of 15362 students from 139 secondary schools in London during the years of 1985-1987; each student is described by 27 attributes including gender, ethnic group, etc. Here we follow the experimental setup as in (Chen, Zhou, and Ye 2011) |
| Dataset Splits | Yes | 10%, 20%, and 30% of the samples from each task are randomly selected as training sets and the rest are used as test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. It only mentions that algorithms were implemented in MATLAB. |
| Software Dependencies | No | The paper states "All algorithms are implemented with MATLAB." but does not provide specific version numbers for MATLAB or any other software dependencies needed to replicate the experiment. |
| Experiment Setup | Yes | In the experiments, the hyper-parameters are tuned by 3-fold cross validation. All algorithms are implemented with MATLAB. The maximum number of iterations is set to 5000 for all algorithms, with tolerance of 10 5. |