Spectral Representation of Robustness Measures for Optimization Under Input Uncertainty
Authors: Jixiang Qing, Tom Dhaene, Ivo Couckuyt
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct multiple experiments to investigate the accuracy of the spectral representation of the robustness measures. We also benchmark their performance for RBO, using the three proposed acquisition functions. The code is implemented using the open-source library Trieste (Berkeley et al., 2021). We conduct RBO on common synthetic benchmark functions and report the normal input uncertainty results in Fig. 3, as well as the uniform input uncertainty results in appendix H.5. |
| Researcher Affiliation | Academia | 1Ghent University, imec, IDLab, Department of Information Technology (INTEC), Tech Lane Zwijnaarde 126, 9052 Ghent, Belgium. Correspondence to: Jixiang Qing <Jixiang.Qing@UGent.be>. |
| Pseudocode | Yes | Algorithm 1 Sampling Robustness Measure s Trajectories |
| Open Source Code | Yes | Our code is available at https://github.com/TsingQAQ/gp_mean_var_rbo. |
| Open Datasets | Yes | We conduct RBO on common synthetic benchmark functions and real-life problems. For each problem, 5d initial randomly generated data are used. |
| Dataset Splits | No | A data set of 10d samples is drawn from a GP prior based on a SE kernel. Afterwards, a GP is constructed and we compare the robustness measure distributions (based on RFF and QFF) at different input locations with an exhaustive Monte Carlo approach, which is regarded as the ground-truth. |
| Hardware Specification | No | The paper does not provide specific details on the hardware used for running the experiments. |
| Software Dependencies | No | The code is implemented using the open-source library Trieste (Berkeley et al., 2021) |
| Experiment Setup | Yes | Number of Fourier features Since Fourier feature-based acquisition functions need to specify the number of features explicitly, we use {128, 900, 1000} for {1d, 2d, 3d} problems, respectively, for both RFF and QFF. For each problem, 5d initial randomly generated data are used. For the GP constructing, the SE ARD kernel is used with a log-normal prior on lengthscales, where the kernel hyperparameter is inferred using maximum a posteriori estimation. Each experiment is repeated 30 times. |