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 [1].
Metric Learning for Ordinal Data
Authors: Yuan Shi, Wenzhe Li, Fei Sha
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that the proposed approach significantly improves baselines that do not explicitly model ordinal features. |
| Researcher Affiliation | Academia | Yuan Shi and Wenzhe Li and Fei Sha Department of Computer Science University of Southern California Los Angeles, CA 90089, USA EMAIL |
| Pseudocode | No | The paper describes the optimization steps in paragraph form, but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | A footnote states 'Can be found at www-scf.usc.edu/ yuanshi/' which refers to Supplementary Materials. This is a personal/university webpage and not a direct link to a source-code repository. |
| Open Datasets | Yes | We use 9 real-world datasets from UCI machine learning repository (Lichman 2013). |
| Dataset Splits | Yes | For each dataset, we randomly split it into the training (60% of samples), validation (20%) and test (20%) sets. |
| Hardware Specification | Yes | On the Car dataset, Ord-LMNN-Uni takes 2 mins and LMNN takes 1 min per hyper-parameter setting (1.4GHz CPU). |
| Software Dependencies | No | The paper does not specify versions for any software components, libraries, or programming languages used in the experiments. |
| Experiment Setup | Yes | Tunable parameters include the tradeoff parameter in LMNN, as well as the tradeoff parameter and margin parameter in our methods. All methods are evaluated using k-nearest neighbor classifier with k set to 3. |