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..
Diversity-Guided Multi-Objective Bayesian Optimization With Batch Evaluations
Authors: Mina Konakovic Lukovic, Yunsheng Tian, Wojciech Matusik
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both synthetic test functions and real-world benchmark problems show that our algorithm predominantly outperforms relevant state-of-the-art methods. |
| Researcher Affiliation | Academia | Mina Konakovi c Lukovi c MIT CSAIL EMAIL Yunsheng Tian MIT CSAIL EMAIL Wojciech Matusik MIT CSAIL EMAIL |
| Pseudocode | Yes | Algorithm 1 DGEMO ... Algorithm 2 Batch Selection Algorithm |
| Open Source Code | Yes | The code is available at https://github.com/yunshengtian/DGEMO. ... Our code will be released open-source with reproducibility guarantee. |
| Open Datasets | Yes | First, we conduct experiments on 13 synthetic multi-objective test functions including ZDT1-3 [54], DTLZ1-6 [10], OKA1-2 [34], VLMOP2-3 [48], which are widely used in previous literature. ... Second, we adopt 7 real-world engineering design problems presented in RE problem suite [46], which are: four bar truss design, reinforced concrete beam design, hatch cover design, welded beam design, disc brake design, gear train design, and rocket injector design. |
| Dataset Splits | No | The paper mentions 'initial samples' and 'batch size' for its Bayesian optimization approach ('50 initial samples', 'batch of 10 samples'), but it does not specify traditional train/validation/test dataset splits with percentages or sample counts for the benchmark problems. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, memory, or cloud computing instance types used for running the experiments. |
| Software Dependencies | No | The paper mentions its implementation is 'built upon pymoo [5], a state-of-the-art Python framework', but it does not specify version numbers for Python, pymoo, or any other software dependencies. |
| Experiment Setup | Yes | For every algorithm, we run every experiment with 10 different random seeds and the same 50 initial samples. In these experiments, we use a batch of 10 samples in each iteration and ran 20 iterations in total. All hyperparameters used are presented in Appendix B. |