ReHLine: Regularized Composite ReLU-ReHU Loss Minimization with Linear Computation and Linear Convergence
Authors: Ben Dai, Yixuan Qiu
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The algorithm is implemented with both Python and R interfaces, and its performance is benchmarked on various tasks and datasets. Our experimental results demonstrate that Re HLine significantly surpasses generic optimization solvers in terms of computational efficiency on large-scale datasets. |
| Researcher Affiliation | Academia | Ben Dai Department of Statistics The Chinese University of Hong Kong bendai@cuhk.edu.hk Yixuan Qiu , School of Statistics and Management Shanghai University of Finance and Economics qiuyixuan@sufe.edu.cn |
| Pseudocode | Yes | Algorithm 1: The Re HLine algorithm that solves (4). |
| Open Source Code | Yes | The source code, project page, accompanying software, and the Python/R interface can be accessed through the link: https://github.com/softmin/Re HLine. |
| Open Datasets | Yes | Specifically, we focus on four classification datasets and five regression datasets sourced from Open ML (https://www.openml.org/)... |
| Dataset Splits | No | No specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning was found. |
| Hardware Specification | No | No specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments were found. |
| Software Dependencies | No | The algorithm is implemented with both Python and R interfaces, and its performance is benchmarked on various tasks and datasets. To achieve a fair comparison, we use a well-organized toolset and framework, the BENCHOPT library [29], to implement optimization benchmarks for all the SOTA solvers. |
| Experiment Setup | Yes | Moreover, we examine the performance of Elastic QR by considering the model defined in (A.2) with λ1 = λ2 = 1, Ridge Huber of (A.4) with λ1 = 0, λ2 = 1, and SVM of (A.1) and s SVM of (A.5) with C = 1. |