Multi-Task Personalized Learning with Sparse Network Lasso
Authors: Jiankun Wang, Lu Sun
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on various synthetic and real-world datasets demonstrate its robustness and effectiveness.Empirical results on both synthetic and real-world datasets demonstrate the superiority of MTPL. |
| Researcher Affiliation | Academia | Jiankun Wang and Lu Sun School of Information Science and Technology Shanghai Tech University, Shanghai, China {wangjk, sunlu1}@shanghaitech.edu.cn |
| Pseudocode | No | The paper describes the optimization algorithm and update procedures but does not contain a structured pseudocode or algorithm block, nor is there a section explicitly labeled 'Algorithm' or 'Pseudocode'. |
| Open Source Code | Yes | We provide the supplementary material of MTPL at: https://github.com/JiankunWang912/MTPL. and We provide the MATLAB code of MTPL at: https://github.com/JiankunWang912/MTPL. |
| Open Datasets | Yes | We conduct experiments on six real-world multi-task datasets: School2, SARCOS3, Sales4, Parkinsons4, Computer5 and Isolet6. Table 1 summarizes their statistics. Details of the datasets are provided in the supplement. 2https://github.com/jiayuzhou/MALSAR/tree/master/data 3http://www.gaussianprocess.org/gpml/data 4https://archive.ics.uci.edu/ml/datasets.php 5https://github.com/probml/pmtk3/tree/master/data 6http://www.cad.zju.edu.cn/home/dengcai/Data/MLData.html |
| Dataset Splits | Yes | For evaluation, we randomly select 60%, 20% and 20% of total samples for training, testing and validation, respectively. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions providing 'MATLAB code' but does not specify any exact software versions for MATLAB itself or any other key software components, libraries, or solvers used for the experiments. |
| Experiment Setup | Yes | The number K of latent bases in GBDSP, VSTG, FORMULA and MTPL is selected from {3, 5, 7, 9, 11}. The value k of similarity function used in Network Lasso, Localized Lasso and MTPL is fixed to be 5. The value k of k-support norm in VSTG is selected from {1, 2, 3}. The number of transfer groups in GBDSP is selected from {3, 5, 7, 9, 11}. The search grid for the other hyper-parameters is set as {2^10, 2^8, ..., 2^8, 2^10}. For each iterative algorithm, we terminate it once the relative change of its objective is below 10^-5, and set the maximum number of iterations as 1000. |