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 [1].
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. |