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..
Linking Heterogeneous Input Features with Pivots for Domain Adaptation
Authors: Guangyou Zhou, Tingting He, Wensheng Wu, Xiaohua Tony Hu
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on a benchmark composed of reviews of 4 types of Amazon products. Experimental results show that our proposed approach significantly outperforms the baseline method, and achieves an accuracy which is competitive with the state-of-the-art methods for sentiment classification adaptation. |
| Researcher Affiliation | Academia | 1 School of Computer, Central China Normal University, Wuhan 430079, China 2 Computer Science Department, University of Southern California, Los Angeles, CA |
| Pseudocode | No | The paper provides mathematical derivations and update equations but does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper does not state that open-source code for the methodology is provided or include a link to a code repository. |
| Open Datasets | Yes | A large majority experiments are performed on the benchmark made of reviews of Amazon products gathered by Blitzer et al. [2007]. |
| Dataset Splits | Yes | All hyper-parameters are set by 5-fold cross validation on the source training set1. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using a 'linear SVM' and that 'implementations provided by the authors' were used for some comparison methods, and that others were 're-implement[ed] based on the original papers', but it does not provide specific version numbers for any software or libraries. |
| Experiment Setup | No | The paper states 'All hyper-parameters are set by 5-fold cross validation on the source training set' but does not provide specific hyperparameter values, optimizer settings, or a detailed description of the training configuration. |