Safe Exploration for Optimization with Gaussian Processes
Authors: Yanan Sui, Alkis Gotovos, Joel Burdick, Andreas Krause
ICML 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate SAFEOPT on synthetic data, as well as two real applications: movie recommendation, and therapeutic spinal cord stimulation. |
| Researcher Affiliation | Academia | Yanan Sui YSUI@CALTECH.EDU California Institute of Technology, Pasadena, CA, USA Alkis Gotovos ALKISG@INF.ETHZ.CH ETH Zurich, Zurich, Switzerland Joel W. Burdick JWB@ROBOTICS.CALTECH.EDU California Institute of Technology, Pasadena, CA, USA Andreas Krause KRAUSEA@ETHZ.CH ETH Zurich, Zurich, Switzerland |
| Pseudocode | Yes | Algorithm 1 SAFEOPT |
| Open Source Code | No | The paper does not provide any specific links or statements about the availability of the source code for the SAFEOPT methodology. |
| Open Datasets | Yes | We test the algorithms on the Movie Lens-100k dataset |
| Dataset Splits | No | The paper does not provide specific training, validation, or test dataset splits (e.g., percentages, sample counts, or references to predefined splits) to reproduce the partitioning of data for model training and evaluation. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory, or specific computer specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions applying 'matrix factorization' and using a 'squared exponential ARD kernel' but does not specify any software names with version numbers (e.g., Python, PyTorch, scikit-learn, or specific solvers). |
| Experiment Setup | Yes | The safety threshold is set equal to the mean of all ratings. We apply a matrix factorization with k = 20 latent factors. We ran both SAFE-UCB and SAFEOPT for T = 100 iterations. |