Stochastic Bayesian Optimization with Unknown Continuous Context Distribution via Kernel Density Estimation
Authors: Xiaobin Huang, Lei Song, Ke Xue, Chao Qian
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Furthermore, we conduct numerical experiments to empirically demonstrate the effectiveness of our algorithms. and In order to empirically evaluate the effectiveness of SBO-KDE and DRBO-KDE, we conduct numerical experiments on synthetic functions and two real-world problems |
| Researcher Affiliation | Academia | National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China School of Artificial Intelligence, Nanjing University, Nanjing 210023, China {huangxb, songl, xuek, qianc}@lamda.nju.edu.cn |
| Pseudocode | Yes | Algorithm 1: SBO-KDE and Algorithm 2: DRBO-KDE |
| Open Source Code | Yes | The code is available at https://github.com/lamda-bbo/sbokde. |
| Open Datasets | Yes | We conduct numerical experiments on four commonly used synthetic test functions (Surjanovic and Bingham 2013), in which we follow the approach of setting some dimensions as context variable from (Williams 2000; Cakmak et al. 2020). and Newsvendor problem is a classic inventory management problem in stochastic optimization and We use the default setting of (Eckman et al. 2021), where s0 = 5, s1 = 9, and w = 1. and The objective function is the posterior mean of a GP trained on 3, 000 samples, which are generated by (Cakmak et al. 2020) from the CVXPortfolio problem (Boyd et al. 2017). |
| Dataset Splits | No | The paper uses an initial data set (n0 points) and then iteratively adds evaluated points, but does not specify a distinct validation set or explicit train/test/validation splits with percentages or sample counts for reproduction. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running its experiments. |
| Software Dependencies | No | The paper mentions software like 'Bo Torch' (Balandat et al. 2020) and techniques like 'L-BFGS (Nocedal 1980)' but does not provide specific version numbers for any software dependencies or libraries needed for replication. |
| Experiment Setup | Yes | We choose the Gaussian kernel K(x) = (2π) Dc/2e x 2 2 for KDE. The bandwidth h(i) t = (4/(Dc + 2))1/(4+Dc)ˆσ(i) t t 1/(4+Dc) based on the rule of thumb (Silverman 1986), where ˆσ(i) t is the standard deviation of the ith dimension of observed context. and The radius of the distribution set is set as δt = t 2/(4+Dc). |