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..
Adaptive Group Sparse Multi-task Learning via Trace Lasso
Authors: Sulin Liu, Sinno Jialin Pan
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on both synthetic and real-world datasets demonstrate the effectiveness of our method in terms of clustering related tasks and generalization performance. |
| Researcher Affiliation | Academia | Sulin Liu and Sinno Jialin Pan Nanyang Technological University, Singapore EMAIL |
| Pseudocode | Yes | Algorithm 1 Optimization procedure for solving (1) |
| Open Source Code | No | The paper does not contain any statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | MDS [Blitzer et al., 2007]: this is a dataset of product reviews on 25 domains (apparel, books, DVD, etc.) crawled from Amazon.com. |
| Dataset Splits | Yes | Training and testing samples are obtained using a 30%70% split. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers required to replicate the experiments. |
| Experiment Setup | No | The paper describes data splitting and task generation methods, but does not provide specific details on hyperparameters, optimizer settings, or other concrete training configurations. |