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].
Discriminative Metric Learning by Neighborhood Gerrymandering
Authors: Shubhendu Trivedi, David Mcallester, Greg Shakhnarovich
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments on a variety of data sets our method is shown to achieve excellent results compared to current state of the art in metric learning. |
| Researcher Affiliation | Academia | Shubhendu Trivedi, David Mc Allester, Gregory Shakhnarovich Toyota Technological Institute Chicago, IL 60637 EMAIL |
| Pseudocode | Yes | Algorithm 1: Stochastic gradient descent; Algorithm 2: Targeted inference; Algorithm 3: Loss augmented inference |
| Open Source Code | No | The paper does not provide an explicit statement or a link to open-source code for the described methodology. |
| Open Datasets | Yes | We compare the error of k NN classifiers using metrics learned with our approach to that with other learned metrics. For this evaluation we replicate the protocol in [11], using the seven data sets in Table 1. |
| Dataset Splits | Yes | for the other five data sets, we report the mean and standard errors of 5-fold cross validation (results for all methods are on the same folds). [...] The value of C is tuned on on a 75%/25% split of the training portion. |
| Hardware Specification | No | The paper mentions running experiments but does not specify any hardware components (e.g., CPU, GPU models, memory). |
| Software Dependencies | No | The paper does not mention any specific software dependencies with version numbers. |
| Experiment Setup | Yes | Our SGD algorithm stops when the running average of the surrogate loss over most recent epoch no longer descreases substantially, or after max. number of iterations. We use learning rate η(t) = 1/t. [...] The value of C is tuned on on a 75%/25% split of the training portion. |