Offline Model-Based Optimization by Learning to Rank
Authors: Rong-Xi Tan, Ke Xue, Shen-Huan Lyu, Haopu Shang, Yao Wang, Yaoyuan Wang, Fu Sheng, Chao Qian
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results across diverse tasks demonstrate the superior performance of our proposed ranking-based method than twenty existing methods. Our implementation is available at https://github.com/ lamda-bbo/Offline-Ra M. In this section, we empirically compare the proposed method with a large variety of previous offline MBO methods on various tasks. |
| Researcher Affiliation | Collaboration | 1 National Key Laboratory for Novel Software Technology, Nanjing University, China 2 School of Artificial Intelligence, Nanjing University, China 3 Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, China 4 College of Computer Science and Software Engineering, Hohai University, China 5 Advanced Computing and Storage Lab, Huawei Technologies Co., Ltd., China |
| Pseudocode | Yes | Algorithm 1 Offline MBO by Learning to Rank |
| Open Source Code | Yes | Our implementation is available at https://github.com/ lamda-bbo/Offline-Ra M. |
| Open Datasets | Yes | We benchmark our method on Design-Bench tasks (Trabucco et al., 2022), including three continuous tasks and two discrete tasks. |
| Dataset Splits | Yes | We split the dataset into a training set and a validation set of the ratio 8 : 2. ... In Design-Bench, the training dataset is selected as the bottom performing x% in the entire collected dataset, (i.e., x = 40, 50, 60). ... We identify the excluded (100 x)% high-scoring data to comprise the OOD dataset for analysis... |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models or cloud instance types) were provided in the paper. The paper mentions using PyTorch for implementation but does not specify the hardware used to run experiments. |
| Software Dependencies | No | The paper mentions using PyTorch (Paszke et al., 2019) and Adam (Kingma & Ba, 2015) but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | We set the size n of training dataset to 10, 000, and following LETOR 4.0 (Qin & Liu, 2013; Qin et al., 2010b), a prevalent benchmark for LTR, we set the list length m = 1000. ... The model is optimized using Adam (Kingma & Ba, 2015) with a learning rate of 3 10 4 and a weight decay coefficient of 1 10 5. After the model is trained... we set η = 1 10 3 and T = 200 for continuous tasks, and η = 1 10 1 and T = 100 for discrete tasks to search for the final design. We use Re LU as activation functions. |