Bayesian Optimization for Unknown Cost-Varying Variable Subsets with No-Regret Costs
Authors: Vu Viet Hoang, Quoc Anh Hoang Nguyen, Hung The Tran
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we show that our proposed algorithm outperforms comparable baselines across a wide range of benchmarks. We conducted an empirical evaluation of our proposed algorithm s performance against baseline methods across a variety of experimental conditions. This included testing on both synthetic and real-world datasets, specifically a plant growth dataset and an airfoil self-noise dataset, which are relevant to the precision agriculture and advanced manufacturing applications discussed earlier. |
| Researcher Affiliation | Collaboration | 1FPT Software AI Center 2Hanoi University of Science and Technology |
| Pseudocode | Yes | Algorithm 1: Proposed method |
| Open Source Code | No | The paper does not contain any explicit statements about providing open-source code, nor does it include links to a code repository. |
| Open Datasets | Yes | (d) a simulator built from the airfoil self-noise dataset (5-D) from the UCI Machine Learning Repository (Dua, Graff et al. 2017). |
| Dataset Splits | No | The paper mentions using synthetic and real-world datasets but does not specify any training, validation, or test dataset splits, percentages, or methodology for splitting the data. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper discusses concepts like Gaussian Processes and Multi-Armed Bandits and mentions specific algorithms such as UCB, TS-PSQ, UCB-PSQ, and UCB-CVS. However, it does not provide specific version numbers for any software libraries, frameworks, or programming languages used in the implementation. |
| Experiment Setup | Yes | In the proposed method, we spend 60 units of cost for the exploration phase. The parameter α at the beginning of the exploitation phase is set to 0.1 and is halved after d function evaluations. |