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..
Regressive Virtual Metric Learning
Authors: Michaël Perrot, Amaury Habrard
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Lastly, we evaluate our approach on several state of the art datasets. and Section 4 is dedicated to an empirical evaluation of our method on several widely used datasets. |
| Researcher Affiliation | Academia | Micha el Perrot, and Amaury Habrard Universit e de Lyon, Universit e Jean Monnet de Saint-Etienne, Laboratoire Hubert Curien, CNRS, UMR5516, F-42000, Saint-Etienne, France. EMAIL |
| Pseudocode | Yes | Algorithm 1: Selecting S from a set of examples S. |
| Open Source Code | Yes | The closed-form implementation of RVML is freely available on the authors website. |
| Open Datasets | Yes | In this section, we evaluate our approach on 13 different datasets coming from either the UCI [19] repository or used in recent works in metric learning [8, 20, 21]. |
| Dataset Splits | Yes | For isolet, splice and svmguide1 we have access to a standard training/test partition, for the other datasets we use a 70% training/30% test partition, we perform the experiments on 10 different splits and we average the result. We set our regularization parameter λ with a 5-fold cross validation. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions algorithms and methods (e.g., 'Sinkhorn-Knopp algorithm', '1-nearest neighbor classifier', 'SCML', 'LMNN') but does not specify software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | We normalize the examples with respect to the training set by subtracting for each attribute its mean and dividing by 3 times its standard deviation. We set our regularization parameter λ with a 5-fold cross validation. After the metric learning step, we use a 1-nearest neighbor classifier to assess the performance of the metric and report the accuracy obtained. and with the parameter σ fixed as the mean of all pairwise training set Euclidean distances |