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.
No-Regret Bayesian Optimization with Unknown Hyperparameters
Authors: Felix Berkenkamp, Angela P. Schoellig, Andreas Krause
JMLR 2019 | Venue PDF | LLM Run Details | Input Tokens: 22,126 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 1,947 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 evaluate our method on several benchmark problems. Keywords: Bayesian optimization, Unknown hyperparameters, Reproducing kernel Hilbert space (RKHS), Bandits, No regret |
| Researcher Affiliation | Academia | Felix Berkenkamp EMAIL Department of Computer Science ETH Zurich Zurich, Switzerland Angela P. Schoellig EMAIL Institute for Aerospace Studies University of Toronto Toronto, Canada Andreas Krause EMAIL Department of Computer Science ETH Zurich Zurich, Switzerland |
| Pseudocode | Yes | Algorithm 1 Adaptive GP-UCB(A-GP-UCB) |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | Yes | Lastly, we use our method to tune a logistic regression problem on the MNIST data set (Le Cun, 1998). |
| Dataset Splits | No | The paper mentions using the MNIST dataset and tuning four training inputs for a logistic regression problem, but it does not provide specific details about how the dataset was split into training, validation, or test sets (e.g., percentages, sample counts, or predefined splits). |
| Hardware Specification | No | The paper does not contain any specific details about the hardware (e.g., CPU, GPU models, memory, or specific computing clusters) used to perform the experiments. |
| Software Dependencies | No | The paper discusses Gaussian processes and other machine learning concepts and methods but does not provide specific version numbers for any software libraries, programming languages, or tools used for implementation. |
| Experiment Setup | Yes | Unless otherwise specified, the initial lengthscales are set to θ0 = 1, the initial norm bound is B0 = 2, the confidence bounds hold with probability at least δ = 0.9, and the tradeofffactor between b(t) and g(t) is λ = 0.1. |