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..
Contextual Outlier Interpretation
Authors: Ninghao Liu, Donghwa Shin, Xia Hu
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on various types of datasets demonstrate the flexibility and effectiveness of the proposed framework. |
| Researcher Affiliation | Academia | Ninghao Liu,1 Donghwa Shin,1 Xia Hu1,2 1Department of Computer Science and Engineering, Texas A&M University 2Center for Remote Health Technologies and Systems, Texas A&M Engineering Experiment Station EMAIL |
| Pseudocode | No | The paper does not contain a clearly labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | We use both real and synthetic datasets in experiments. ... The real-world datasets used in our experiments include Wisconsin Breast Cancer (WBC) dataset [Asuncion and Newman, 2007], MNIST dataset and Twitter spammer dataset [Yang et al., 2011]. |
| Dataset Splits | Yes | The parameters of SVMs are tuned by validation, where some samples from Oi and Ci are randomly selected as the validation set. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models) used for running the experiments. |
| Software Dependencies | No | The paper mentions tools like SVMs, RBM, and neural networks but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | No | The paper mentions that 'parameters of SVMs are tuned by validation', but it does not provide specific hyperparameter values (e.g., learning rate, batch size) or detailed system-level training settings. |