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..
Multi-Task Personalized Learning with Sparse Network Lasso
Authors: Jiankun Wang, Lu Sun
IJCAI 2022 | Venue PDF | 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 EMAIL |
| 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. |