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 [1].
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. |