Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
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 | Input Tokens: 18,439 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,245 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |