No-Regret Bayesian Optimization with Unknown Hyperparameters
Authors: Felix Berkenkamp, Angela P. Schoellig, Andreas Krause
JMLR 2019 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| 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. |