Bayesian Optimization with Switching Cost: Regret Analysis and Lookahead Variants
Authors: Peng Liu, Haowei Wang, Wei Qiyu
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In addition, the empirical performance of the proposed algorithm is tested based on both synthetic and real data experiments, and it shows that our cost-aware non-myopic algorithm performs better than other popular alternatives. |
| Researcher Affiliation | Collaboration | Peng Liu1 , Haowei Wang2 , Wei Qiyu3 1Singapore Management University 2Rice-Rick Digitalization 3Shanghai University liupeng@smu.edu.sg, haowei wang@ricerick.com, qywei@shu.edu.cn |
| Pseudocode | Yes | Algorithm 1: dist UCB: distance-adjusted UCB |
| Open Source Code | No | The paper does not provide a link to open-source code or explicitly state that the code is publicly available. |
| Open Datasets | Yes | Using a neural network model, we empirically evaluate dist UCB on two hyperparameter tuning tasks for training a two-layer feed-forward neural network on two popular UCI datasets: breast cancer and spam |
| Dataset Splits | Yes | For both data sets, we allocate 70% to training and 30% to the test set. |
| 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 software like PyTorch in its references, but it does not specify any software dependencies or their version numbers used in the experimental setup. |
| Experiment Setup | Yes | The total iteration budget N for each experiment is set to N = n0 + 100, where n0 denotes the number of initial design points and is set as 20, 40, and 60 for the three synthetic functions, respectively. In addition, we add a homogeneous noise with a standard normal distribution with a standard deviation of 0.1, and set up 32 simulations in each Monte Carlo approximation to keep the same setting as EIpu. For a fair comparison, each method s number of lookahead steps is set to 3. ... We consider tuning four hyperparameters: batch size, initial learning rate, learning rate schedule, and the number of hidden dimensions. For each of the four hyperparameters to be adjusted, the adjustment range is given: [32, 128], [1e 6, 1.0], [1e 6, 1.0], [0.5, 4]. ... Each iteration repeats 5 times. |