Bayesian Optimization using Pseudo-Points
Authors: Chao Qian, Hang Xiong, Ke Xue
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments using UCB and other acquisition functions, i.e., probability of improvement (PI) and expectation of improvement (EI), on synthetic as well as real-world problems clearly show the advantage of generating pseudo-points. |
| Researcher Affiliation | Academia | 1National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China 2University of Science and Technology of China, Hefei 230027, China |
| Pseudocode | Yes | Algorithm 1 BO Framework; Algorithm 2 BO-PP Framework |
| Open Source Code | No | The paper does not provide a statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper mentions common synthetic and real-world datasets like 'Dropwave, Griewank, Hart6, Rastrigin', 'Wine quality', 'Breast cancer', and 'Boston housing'. However, it does not provide concrete access information such as URLs, DOIs, specific repository names, or formal citations with authors and years for these datasets. |
| Dataset Splits | Yes | All data sets are randomly split into training/validation/test sets with ratio 0.7/0.2/0.1 |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments (e.g., GPU models, CPU types, or memory). |
| Software Dependencies | No | The paper mentions the 'ARD squared exponential kernel' and the 'DIRECT algorithm', but it does not provide specific version numbers for any software libraries, programming languages, or tools used in the implementation. |
| Experiment Setup | Yes | The noise level is set to σ2 = 0.0001, and the iteration budget is set to 100. In the (t + 1)-th iteration of BO-PP, for each point in Dt, one pseudo-point is generated by randomly sampling within its distance τt and taking the same function value; thus, lt = |Dt|. To control the error of objective values with pseudo-points increasing, τt is set to rτ0/(dlt), which decreases with lt. Note that r corresponds to the width of each dimension of the search domain. τ0 is set to a small value. We will use 0.01, 0.001 and 0.0001 to explore its influence, and the corresponding algorithms are denoted as BO-PP01, BO-PP001 and BO-PP0001, respectively. For UCB, βt in Eq. (5) is set to 2 log(td/2+2π2/3δ) where δ = 0.1 |