Minimum Regret Search for Single- and Multi-Task Optimization
Authors: Jan Hendrik Metzen
ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide empirical results both for a synthetic single-task optimization problem as well as for a simulated multi-task robotic control problem. |
| Researcher Affiliation | Collaboration | Jan Hendrik Metzen JANMETZEN@MAILBOX.ORG Universit at Bremen, 28359 Bremen, Germany Corporate Research, Robert Bosch Gmb H, 70442 Stuttgart, Germany |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | Source code for replicating the reported experiment is available under https://github.com/jmetzen/bayesian_optimization. |
| Open Datasets | No | The paper describes generating its own synthetic dataset for the single-task benchmark, and for the multi-task robotic control problem, it uses a simulated environment. There is no concrete access information (link, DOI, citation) provided for a publicly available or open dataset. |
| Dataset Splits | No | The paper describes using a set of 250 generated functions for testing and evaluating on '16 test contexts', but it does not specify explicit training/validation/test dataset splits with percentages or sample counts in the traditional sense, as data is acquired sequentially. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | The paper mentions software components and algorithms like Gaussian processes, RBF and Matérn kernels, DIRECT, L-BFGS, and DMP, but does not provide specific version numbers for any of these software dependencies or libraries. |
| Experiment Setup | Yes | Gaussian noise with standard deviation σ = 10 3 is added to each observation. The GP used as surrogate model in the optimizer employed the same isotropic RBF kernel with fixed, identical hyperparameters. ... we used nf = 1000, nr = 25, and ny = 51. ... UCB s exploration parameter κ is set to a constant value of 5.0. |