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..
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. |