Summary of Pacsbo: Probably Approximately Correct Safe Bayesian Optimization, by Abdullah Tokmak et al.
PACSBO: Probably approximately correct safe Bayesian optimization
by Abdullah Tokmak, Thomas B. Schön, Dominik Baumann
First submitted to arxiv on: 2 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY); Optimization and Control (math.OC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a new approach to safe Bayesian optimization (BO) that estimates an upper bound on the norm of an unknown function in a reproducing kernel Hilbert space (RKHS). The algorithm, called PACSBO, is designed for probably approximately correct safe BO and addresses the challenge of obtaining an upper bound of an unknown function in its corresponding RKHS. By treating the RKHS norm as a local rather than global object, the algorithm reduces conservatism while still guaranteeing safety with high probability. This approach has been numerically and experimentally validated on various control tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it possible to find the best way to control something without knowing all the rules that govern its behavior. It uses a method called Bayesian optimization to do this, but also adds a “safety net” to make sure the controller doesn’t get stuck in an unwanted state. The new approach is more flexible and works better than previous methods on real-world problems. |
Keywords
» Artificial intelligence » Optimization » Probability
