Safe Active Learning for Time-Series Modeling with Gaussian Processes
Authors: Christoph Zimmer, Mona Meister, Duy Nguyen-Tuong
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We analyze the proposed algorithm and evaluate it empirically on a technical application. The results show the effectiveness of our approach in a realistic technical use case. In Section 5, we highlight our empirical evaluations in learning time-series model in several settings. |
| Researcher Affiliation | Industry | Christoph Zimmer Mona Meister Duy Nguyen-Tuong Bosch Center for Artificial Intelligence, Renningen, Germany {christoph.zimmer,mona.meister,duy.nguyen-tuong}@de.bosch.com |
| Pseudocode | Yes | Algorithm 1 Safe Active Learning for Time-Series Modeling |
| Open Source Code | No | The paper does not provide an explicit statement or link to the open-source code for the described methodology. |
| Open Datasets | No | The paper describes using 'synthetic models' and data from a 'high-pressure fluid injection system' (a technical use case), but does not provide concrete access information like links, DOIs, or citations to publicly available datasets. |
| Dataset Splits | No | The paper mentions collecting initial safe trajectories ('10 initial safe trajectories') and updating models, but does not provide specific details on train/validation/test dataset splits (e.g., percentages, counts, or a specific splitting methodology) needed for reproduction. |
| Hardware Specification | No | The paper states, 'Our experiments are performed on a desktop computer.' This is a general description and does not provide specific hardware details such as CPU/GPU models, memory, or other specifications. |
| Software Dependencies | No | The paper mentions using a 'GP model' and 'Monte-Carlo sampling' but does not specify any software names with version numbers (e.g., Python 3.x, specific libraries, or simulation software versions) used in the experiments. |
| Experiment Setup | Yes | For simplicity we employ ramps for the piecewise trajectory parametrization, but other curve parameterizations could also be used instead, e.g. spline parameterization. The piecewise trajectory is again parametrized as 4D-ramps with m=5. We initialize the models using 10 collected piecewise ramps in a safe area, and start exploring in the input space. For computing the safety condition ξ(τ) from Eq. (5), we employ Monte-Carlo sampling. We update the hyperparameters after every iteration. |