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..
Instance-Level Metalearning for Outlier Detection
Authors: Long Vu, Peter Kirchner, Charu C. Aggarwal, Horst Samulowitz
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This section presents the experimental evaluation of T-Auto OD on a variety of data sets. |
| Researcher Affiliation | Collaboration | Long Vu , Peter Kirchner , Charu C. Aggarwal , Horst Samulowitz IBM Research EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 T-Auto OD-Train and Algorithm 2 T-Auto OD-Test |
| Open Source Code | Yes | 5https://github.com/t-autood/t-autood |
| Open Datasets | Yes | All data sets were derived from Open ML 6, and were generated from classification data sets (after possible sparsification). 6https://www.openml.org |
| Dataset Splits | Yes | We then split these 520 derived data sets into a training set of 416 derived data sets and testing set of 104 derived data sets. The LGBM model was then trained using the training/validation data of the holdout partitions. |
| Hardware Specification | No | The paper does not mention any specific hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions 'scikit-learn open-source package', 'LGBM classifier', and 'XGB model', but does not provide specific version numbers for any of these software dependencies. |
| Experiment Setup | Yes | The hyper-parameters for the pipelines were chosen at random from a recommended range as suggested in the scikit-learn implementations of these algorithms. We used an out-of-the-box LGBM classifier4 with its default hyperparameters in this paper. The value of d used was 20. Furthermore, we calculated the Mahalanobis distance of data instances in the data sets and added this distance as an additional feature. |