Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Online Learning from Capricious Data Streams: A Generative Approach
Authors: Yi He, Baijun Wu, Di Wu, Ege Beyazit, Sheng Chen, Xindong Wu
IJCAI 2019 | Venue PDF | LLM Run Details | Input Tokens: 16,239 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,086 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results demonstrate that OCDS achieves conspicuous performance on both synthetic and real datasets. and 6 Experiments We use 15 UCI datasets [Dua and Karra Taniskidou, 2017] and 1 real-world IMDB dataset [Maas et al., 2011] to evaluate the performance of OCDS. |
| Researcher Affiliation | Academia | 1School of Computing and Informatics, University of Louisiana at Lafayette, USA 2Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, China |
| Pseudocode | Yes | Algorithm 1: Retrieval Strategy and Algorithm 2: The OCDS algorithm |
| Open Source Code | No | The paper does not include an explicit statement about releasing source code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | We use 15 UCI datasets [Dua and Karra Taniskidou, 2017] and 1 real-world IMDB dataset [Maas et al., 2011] to evaluate the performance of OCDS. |
| Dataset Splits | No | The paper does not provide explicit details about training, validation, or test dataset splits (e.g., percentages, sample counts, or specific pre-defined splits). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or library versions used for the experiments. |
| Experiment Setup | Yes | To find the best settings of the parameters α, β1 and β2, we use grid searches ranging from 10 5 to 1. For memory and running time efficiency, we let |Ut| 150 by setting γ in different datasets. ... The ratio of the maximal removed features is denoted as VI . For example, VI = 0.5 means that at most 50% of features in xt are randomly removed. The default value of VI is 0.5 in our experiments. |