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.
Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs
Authors: Raul Astudillo, Daniel Jiang, Maximilian Balandat, Eytan Bakshy, Peter Frazier
NeurIPS 2021 | Venue PDF | LLM Run Details | Input Tokens: 19,064 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,083 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 | An empirical evaluation on a number of synthetic and real-world experiments demonstrates that B-MS-EI performs favorably with respect to other acquisition functions that are widely-used in settings with heterogeneous costs. |
| Researcher Affiliation | Collaboration | Raul Astudillo Cornell University EMAIL Daniel R. Jiang Facebook EMAIL Maximilian Balandat Facebook EMAIL Eytan Bakshy Facebook EMAIL Peter I. Frazier Cornell University EMAIL |
| Pseudocode | No | The paper describes algorithms and formulations but does not include a distinct pseudocode block or an algorithm box. |
| Open Source Code | Yes | An implementation of our algorithms and numerical experiments can be found at https://github.com/RaulAstudillo06/BudgetedBO. |
| Open Datasets | Yes | The LDA and CNN problems use publicly available data sets from the HPOLib (The HPOlib authors, 2014) and HPOLib1.5 (The HPOlib1.5 authors, 2017) hyperparameter optimization libraries. ... The second data set was obtained by training a 3-layer convolutional neural network on the CIFAR-10 dataset... Finally, the RF-Boston problem considers optimization of the 5-fold cross validation error when using a random forest (RF) regressor on the Boston dataset, both of which are from sklearn (Pedregosa et al., 2011). |
| Dataset Splits | Yes | The RF-Boston problem considers optimization of the 5-fold cross validation error when using a random forest (RF) regressor on the Boston dataset |
| Hardware Specification | No | The paper mentions |
| Software Dependencies | No | The paper mentions |
| Experiment Setup | Yes | For instance, in the context of hyperparameter optimization of machine learning algorithms (Swersky et al., 2013; Wu et al., 2020), certain values of hyperparameters such as the learning rate may yield longer training times. ... We tune the following hyperparameters of the RF regressor: n_estimators [1, 256] (rounded to the nearest integer), max_depth [1, 64] (rounded to nearest integer), and max_features [0.1, 1] (on a log10 scale). |