SnAKe: Bayesian Optimization with Pathwise Exploration
Authors: Jose Pablo Folch, Shiqiang Zhang, Robert Lee, Behrang Shafei, David Walz, Calvin Tsay, Mark van der Wilk, Ruth Misener
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | For all experimental results we report the mean and the standard deviation over 25 experimental runs. We give the full implementation details and results in Appendix E and F respectively. Classical BO methods are implemented using Bo Torch [3] and GPy Torch [11]. The code to replicate all results is available online at https://github.com/cog-imperial/Sn AKe. |
| Researcher Affiliation | Collaboration | Jose Pablo Folch Imperial College London Shiqiang Zhang Imperial College London Robert M Lee BASF SE Ludwigshen, Germany Behrang Shafei BASF SE Ludwigshafen, Germany Calvin Tsay Imperial College London Mark van der Wilk Imperial College London Ruth Misener Imperial College London |
| Pseudocode | Yes | Algorithm 1 -Point Deletion (page 5) and Algorithm 2 Sn AKe (page 6) are explicitly labeled and structured algorithm blocks. |
| Open Source Code | Yes | The code to replicate all results is available online at https://github.com/cog-imperial/Sn AKe. |
| Open Datasets | No | The paper uses synthetic functions (e.g., Branin2D, Hartmann3D) and refers to the 'Sn Ar chemistry benchmark [18]' and 'Schekel benchmark function (as in [41])'. While these are known functions/benchmarks, the paper does not provide concrete access information (specific link, DOI, or repository) for the *data instances* used for training, nor does it explicitly state that the generated data is publicly available. |
| Dataset Splits | No | The paper does not explicitly provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology) for train/validation/test sets. |
| Hardware Specification | Yes | We were able to comfortably run all experiments in a CPU (2.5 GHz Quad-Core Intel Core i7), where Sn AKe shared a wall-time similar to Local Penalization methods. |
| Software Dependencies | No | The paper mentions software like Bo Torch [3], GPy Torch [11], PyTorch [36], and the Summit package [10]. However, it does not provide specific version numbers for these software dependencies within the main text or appendices. |
| Experiment Setup | Yes | For all experimental results we report the mean and the standard deviation over 25 experimental runs. We give the full implementation details and results in Appendix E and F respectively. [...] We set a delay of tdelay = 25, and optimize for T = 100 iterations. [...] For every experiment, T = 250, and we limit the x-axis to the maximum cost achieved by Sn AKe or Random. [...] In all experiments, we examine Sn AKe for = 0, 0.1, and 1. We further introduce a parameter-free alternative by adaptively selecting to be the smallest length scale from the GP s kernel, and denote it -Sn AKe. |