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].
Efficient Learning of Mahalanobis Metrics for Ranking
Authors: Daryl Lim, Gert Lanckriet
ICML 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To evaluate our proposed method, we conducted two sets of experiments. |
| Researcher Affiliation | Academia | Daryl K. H. Lim EMAIL Department of Electrical and Computer Engineering, University of California, San Diego, CA 92093 USA |
| Pseudocode | Yes | Algorithm 1 Symmetric gradient update |
| Open Source Code | No | The paper does not provide a link to its own source code or explicitly state that it is open-source. It references a third-party code for LMNN. |
| Open Datasets | Yes | Image Net (Deng et al., 2009), CAL10K (Tingle et al., 2010) and Magna Tagatune (Law et al., 2009)... covertype dataset from the UCI repository |
| Dataset Splits | Yes | For each method, the hyperparameters with the best MAP performance on the validation set were selected. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware (e.g., CPU, GPU models, memory, or cloud resources) used for running the experiments. |
| Software Dependencies | No | The paper mentions various algorithms and implementations used (e.g., MLR-ADMM, LMNN 2.4 code) but does not provide specific version numbers for its own software dependencies or general software environment required for replication. |
| Experiment Setup | Yes | For both variants of FRML, the trade-off parameter λ was fixed at 0.1 and we used a minibatch of size 5. |