Scalable Global Optimization via Local Bayesian Optimization
Authors: David Eriksson, Michael Pearce, Jacob Gardner, Ryan D. Turner, Matthias Poloczek
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | A comprehensive evaluation demonstrates that Tu RBO outperforms stateof-the-art methods from machine learning and operations research on problems spanning reinforcement learning, robotics, and the natural sciences. |
| Researcher Affiliation | Collaboration | David Eriksson Uber AI eriksson@uber.com Michael Pearce University of Warwick m.a.l.pearce@warwick.ac.uk Jacob R Gardner Uber AI jake.gardner@uber.com Ryan Turner Uber AI ryan.turner@uber.com Matthias Poloczek Uber AI poloczek@uber.com |
| Pseudocode | No | The paper describes the algorithm in prose but does not include a formal pseudocode block or algorithm listing. |
| Open Source Code | Yes | An implementation of Tu RBO is available at https://github.com/uber-research/Tu RBO. |
| Open Datasets | Yes | We evaluate Tu RBO on a wide range of problems: a 14D robot pushing problem, a 60D rover trajectory planning problem, a 12D cosmological constant estimation problem, a 12D lunar landing reinforcement learning problem, and a 200D synthetic problem. The 14D robot pushing problem is considered in Wang et al. [45]. The rover trajectory planning problem is from [45]. The cosmological constant problem uses a physics simulator1 (https://lambda.gsfc.nasa.gov/toolbox/lrgdr/). The lunar lander is implemented in the Open AI gym2 (https://gym.openai.com/envs/Lunar Lander-v2). The 200-dimensional Ackley function is a standard synthetic problem. |
| Dataset Splits | Yes | We choose 20 uniformly distributed hypercubes of (base) side length 0.4, each containing 200 uniformly distributed training points. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python 3.x, library 1.x). |
| Experiment Setup | Yes | We run each method for a total of 10K evaluations and batch size of q = 50. Tu RBO-1 and all other methods are initialized with 100 points except for Tu RBO-20 where we use 50 initial points for each trust region. |