A Unified Framework for Rank-based Loss Minimization
Authors: Rufeng Xiao, Yuze Ge, Rujun Jiang, Yifan Yan
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments conducted on synthetic and real datasets illustrate the effectiveness and efficiency of the proposed algorithm. |
| Researcher Affiliation | Academia | Rufeng Xiao Yuze Ge Rujun Jiang Yifan Yan School of Data Science, Fudan University {rfxiao21,yzge23}@m.fudan.edu.cn rjjiang@fudan.edu.cn yanyf21@m.fudan.edu.cn |
| Pseudocode | Yes | Algorithm 1 ADMM framework. Algorithm 2 A refined pool-adjacent-violators algorithm for solving problem (10) |
| Open Source Code | Yes | The source code is available in the https://github.com/Rufeng Xiao/ADMM-for-rank-based-loss. |
| Open Datasets | Yes | We utilize the datasets.make_classification() function from the Python package scikit-learn [38] to generate two-class classification problem datasets of various dimensions. The SVMguide [26] dataset, frequently used in support vector machines, is included in our experiments. We also employ AD [29], which comprises potential advertisements for Internet pages, and Monks [50], the dataset based on the MONK s problem that served as the first international comparison of learning algorithms. The Splice dataset from the UCI [18] is used for the task of recognizing DNA sequences as exons or introns. We additionally include Australian , Phoneme , and Titanic dataset from [17]. Lastly, the UTKFace dataset [53] is used to predict gender based on facial images. |
| Dataset Splits | Yes | Consistent with their experimental setup, we randomly split each dataset into a 50% training set, a 25% validation set, and a 25% test set. |
| Hardware Specification | Yes | All algorithms are implemented in Python 3.8 and all the experiments are conducted on a Linux server with 256GB RAM and 96-core AMD EPYC 7402 2.8GHz CPU. |
| Software Dependencies | No | The paper states 'All algorithms are implemented in Python 3.8' but does not provide specific version numbers for other key software components or libraries used in the experiments. |
| Experiment Setup | Yes | In the experiments, we set the maximum iteration limit in our algorithm to 300. We set the γ in Section 4 to γk = max{10 5 0.9k, 10 9}. For simplicity, we set the regularization parameter µ as 0.01 and q as 0.8. Both SGD and LSVRG are run for 2000 epochs and the batch size of SGD is set to 64, and the epoch lengths are set to 100 as recommended in [36]. We set γ = 0.61 and δ = 0.69 in (5) based on the recommendations in [45]. We set B = log (1 + exp ( 5)) in (4). |