Safety guarantees for iterative predictions with Gaussian Processes
Gaussian Processes (GPs) are widely employed in control and learning because of their principled treatment of uncertainty. However, tracking uncertainty for iterative, multistep predictions in general leads to an analytically intractable problem. While approximation methods exist, they do not come w...
| Главные авторы: | , , , , , , |
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| Формат: | Conference item |
| Язык: | English |
| Опубликовано: |
IEEE
2021
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