DOFEN: Deep Oblivious Forest ENsemble
Authors: KuanYu Chen, Ping-Han Chiang, Hsin-Rung Chou, Chih-Sheng Chen, Tien-Hao Chang
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | DOFEN surpasses other DNNs on tabular data, achieving state-of-the-art performance on the well-recognized benchmark: Tabular Benchmark [1], which includes 73 total datasets spanning a wide array of domains. ... To evaluate DOFEN comprehensively and objectively, we have chosen a recent and well-recognized benchmark: the Tabular Benchmark [1]. ... Section 4 Experiments |
| Researcher Affiliation | Collaboration | Kuan-Yu Chen Sinopac Holdings lavamore@sinopac.com Ping-Han Chiang Sinopac Holdings u10000129@gmail.com Hsin-Rung Chou Sinopac Holdings sherry.chou@sinopac.com Chih-Sheng Chen Sinopac Holdings sheng77@sinopac.com Darby Tien-Hao Chang Sinopac Holdings National Cheng Kung University darby@sinopac.com |
| Pseudocode | Yes | Algorithm 1: Two-level Relaxed ODT Ensemble |
| Open Source Code | Yes | The code of DOFEN is available at: https: //github.com/Sinopac-Digital-Technology-Division/DOFEN. |
| Open Datasets | Yes | We strictly follow the protocols of the Tabular Benchmark as detailed in its official implementation1. ... For full details, please refer to the original paper [1]. The Tabular Benchmark categorized datasets into classification and regression... Footnote 1: https://github.com/Leo Grin/tabular-benchmark. Appendix B.3 Mappings of Open ML Task ID and Dataset Name |
| Dataset Splits | Yes | We strictly follow the protocols of the Tabular Benchmark as detailed in its official implementation1. This includes dataset splits, preprocessing methods, hyperparameter search guidelines, and evaluation metrics. |
| Hardware Specification | Yes | The experiments involving DNN-based models were performed using an NVIDIA Ge Force RTX 2080 Ti, while those for the GBDT-based models utilized an AMD EPYC 7742 64-core Processor with 16 threads. ... This experiment was conducted using a single NVIDIA Tesla V100 GPU. ... Appendix H.1: GPUs: NVIDIA Ge Force RTX 2080 Ti, NVIDIA DGX1, NVIDIA A100. CPUs: Intel(R) Xeon(R) Silver 4210 CPU, Intel(R) Xeon(R) CPU E5-2698 v4, AMD EPYC605 7742 64-core Processor |
| Software Dependencies | No | DOFEN is implemented in Pytorch [31]. (no specific version number is provided for PyTorch or other mentioned libraries like Light GBM, Cat Boost, XGBoost used in the benchmark). |
| Experiment Setup | Yes | For hyperparameters used in model optimization (e.g. optimizer, learning rate, weight decay, etc.), all experiments share the same settings. Specifically, DOFEN uses Adam W optimizer [32] with 1e 3 learning rate and no weight decay. The batch size is set to 256, and DOFEN is trained for 500 epochs without using learning rate scheduling or early stopping. ... Table 2: The default hyperparameters of DOFEN. |