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].
Learning by Minimizing the Sum of Ranked Range
Authors: Shu Hu, Yiming Ying, xin wang, Siwei Lyu
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical results highlight the effectiveness of the proposed optimization framework and demonstrate the applicability of proposed losses using synthetic and real datasets. |
| Researcher Affiliation | Collaboration | Shu Hu University at Buffalo, SUNY EMAIL Yiming Ying University at Albany, SUNY EMAIL Xin Wang Cura Cloud Corporation EMAIL Siwei Lyu University at Buffalo, SUNY EMAIL |
| Pseudocode | Yes | Algorithm 1: DCA for Minimizing So RR |
| Open Source Code | Yes | Code available at https://github.com/discovershu/So RR. |
| Open Datasets | Yes | We use five benchmark datasets from the UCI [10] and the KEEL [1] data repositories; We use the MNIST dataset[18] |
| Dataset Splits | Yes | For each dataset, we first randomly select 50% samples for training, and the remaining 50% samples are randomly split for validation and testing (each contains 25% samples). |
| Hardware Specification | No | The paper describes the experimental setup and results but does not provide any specific details about the hardware (e.g., GPU models, CPU types, memory) used to run the experiments. |
| Software Dependencies | No | The paper describes the algorithms and their application but does not specify any software dependencies with version numbers (e.g., 'Python 3.8', 'PyTorch 1.9') that would be needed for replication. |
| Experiment Setup | Yes | Hyper-parameters C, k, and m are selected based on the validation set. Specifically, parameter C is chosen from {100, 101, 102, 103, 104, 105}, parameter k {1} [0.1 : 0.1 : 1]n, where n is the number of training samples, and parameter m are selected in the range of [1, k). |