reproducibilityindex.ai

Optimistic Bayesian Optimization with Unknown Constraints

Authors: Quoc Phong Nguyen, Wan Theng Ruth Chew, Le Song, Bryan Kian Hsiang Low, Patrick Jaillet

Reproducibility Variable	Result	LLM Response
Research Type	Experimental	The performance of our proposed algorithms is also empirically evaluated using several synthetic and real-world optimization problems.
Researcher Affiliation	Academia	1LIDS and EECS, Massachusetts Institute of Technology, USA 2School of Computing, National University of Singapore, Singapore
Pseudocode	Yes	Algorithm 1 UCB-D
Open Source Code	Yes	Regarding the experimental results, we have included both the code and the datasets in the submission.
Open Datasets	Yes	Regarding the experimental results, we have included both the code and the datasets in the submission. ... In the [CNN] problem, a two-layer CNN is trained on a class-imbalanced CIFAR10 dataset.
Dataset Splits	No	The paper does not explicitly provide details about training, validation, or test dataset splits in the main text.
Hardware Specification	No	The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments.
Software Dependencies	No	The paper does not provide specific software dependencies with version numbers needed to replicate the experiment.
Experiment Setup	No	The paper does not provide specific experimental setup details such as concrete hyperparameter values or training configurations in the main text.