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..
Optimistic Bayesian Optimization with Unknown Constraints
Authors: Quoc Phong Nguyen, Wan Theng Ruth Chew, Le Song, Bryan Kian Hsiang Low, Patrick Jaillet
ICLR 2024 | Venue PDF | LLM Run Details
| 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. |