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..
Transfer Learning with Active Queries from Source Domain
Authors: Sheng-Jun Huang, Songcan Chen
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The effectiveness of the proposed method is validated by experiments on 15 datasets for sentiment analysis and text categorization. |
| Researcher Affiliation | Academia | Sheng-Jun Huang and Songcan Chen College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, 211106 EMAIL |
| Pseudocode | Yes | Algorithm 1 The TLAS Algorithm |
| Open Source Code | No | The paper does not provide any explicit statement or link for open-source code release. |
| Open Datasets | Yes | The Sentiment Analysis dataset1 contains product reviews on Amazon from four domains: Book, DVD, Electronics and Kitchen. For each domain, 1000 positive reviews and 1000 negative reviews are collected. Each review text is represented by a 200 dimensional feature vector according to [Chattopadhyay et al., 2013]. ... For the text categorization task, we use a preprocessed subset of Reuters-215782 as in [Dai et al., 2007]. 1http://www.cs.jhu.edu/ mdredze/datasets/sentiment 2http://www.cse.ust.hk/TL/dataset/Reuters.zip |
| Dataset Splits | Yes | For each dataset, we randomly divide the source domain data into two parts: 10% as the labeled set SL, and the rest 90% as the unlabeled set SU. Similarly, the target domain data is randomly divided into three parts: 50% for testing, 10% as the labeled set TL, and the rest 40% as the unlabeled set TU. |
| Hardware Specification | No | The paper does not specify the hardware used for running the experiments. |
| Software Dependencies | No | We employ Lib SVM [Chang and Lin, 2011] with default parameters to implement the classification model. (No version number for Lib SVM is provided). |
| Experiment Setup | Yes | In our experiments, we set n Q = 10 and λ = 10 as default for all datasets, and compute the kernel matrix K using RBF kernel with default parameters. |