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..
Handling Learnwares Developed from Heterogeneous Feature Spaces without Auxiliary Data
Authors: Peng Tan, Zhi-Hao Tan, Yuan Jiang, Zhi-Hua Zhou
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real-world tasks validate the efficacy of our method. |
| Researcher Affiliation | Academia | Peng Tan , Zhi-Hao Tan , Yuan Jiang and Zhi-Hua Zhou National Key Laboratory for Novel Software Technology Nanjing University, Nanjing 210023, China EMAIL |
| Pseudocode | Yes | The overall procedure is sketched in Algorithms 1 and 2. |
| Open Source Code | Yes | https://github.com/LAMDA-TP/Heterogeneous-learnware-without-auxiliary-data |
| Open Datasets | Yes | We conduct empirical experiments on six heterogeneous learnware scenarios involving five real-world tasks: Mfeat [van Breukelen et al., 1998], Anuran [Colonna et al., 2012], Digits [Garris et al., 1997], Kddcup99 [Lippmann et al., 2000] and Covtype [Blackard and Dean, 1999]. |
| Dataset Splits | Yes | The dimension of subspace is chosen by cross validation. |
| Hardware Specification | No | The paper does not specify the hardware used for experiments, such as specific GPU or CPU models. |
| Software Dependencies | No | The paper mentions testing 'several model types like SVM and random forest' but does not specify software dependencies with version numbers (e.g., Python version, library versions). |
| Experiment Setup | Yes | In our experiment, parameters are set as follows: the reduced set size mi is 10... For subspace learning, the trade-off parameters is set as α = 10 5, γ = 1, the max iteration is t = 500 and the learning rate is η = 10 2. The dimension of subspace is chosen by cross validation. We test several model types like SVM and random forest. All experiments are repeated 50 times. |