Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Minimum Regret Search for Single- and Multi-Task Optimization
Authors: Jan Hendrik Metzen
ICML 2016 | Venue PDF | 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 EMAIL 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. |