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.
Fast Information-theoretic Bayesian Optimisation
Authors: Binxin Ru, Michael A. Osborne, Mark Mcleod, Diego Granziol
ICML 2018 | Venue PDF | LLM Run Details | Input Tokens: 15,810 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,536 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 | We demonstrate empirically that FITBO inherits the performance associated with informationtheoretic Bayesian optimisation, while being even faster than simpler Bayesian optimisation approaches, such as Expected Improvement. We conduct a series of experiments to test the empirical performance of FITBO and compare it with other popular acquisition functions. |
| Researcher Affiliation | Collaboration | 1Department of Engineering Science, University of Oxford, Oxford, UK 2Mind Foundry Ltd., Oxford. |
| Pseudocode | Yes | Algorithm 1 FITBO acquisition function |
| Open Source Code | Yes | Our Matlab code for FITBO will be available at https: //github.com/rubinxin/FITBO. |
| Open Datasets | Yes | We perform optimisation tasks on three challenging benchmark functions: Branin (defined in [0, 1]2), Eggholder (defined in [0, 1]2) and Hartmann (defined in [0, 1]6)...Boston housing dataset (Bache and Lichman, 2013)...validation set of the MNIST dataset (Le Cun et al., 1998)...breast cancer dataset (Bache and Lichman, 2013). |
| Dataset Splits | Yes | The dataset is randomly partitioned into train/validation/test sets...We compute the median IR and the median L 2 over 40 random initialisations. |
| Hardware Specification | Yes | All the timing tests were performed exclusively on a 2.3 GHz Intel Core i5. |
| Software Dependencies | No | The paper mentions 'Matlab code' but does not provide specific version numbers for Matlab or any other software dependencies. |
| Experiment Setup | Yes | In all tests, we set the observation noise to σ2 n = 10 3 and resample all the hyperparameters after each function evaluation. We initialise all Bayesian optimisation algorithms with 3 random observation data and set the observation noise to σ2 n = 10 3. All experiments are repeated 40 times. |