Summary of Optimistic Safety For Online Convex Optimization with Unknown Linear Constraints, by Spencer Hutchinson et al.
Optimistic Safety for Online Convex Optimization with Unknown Linear Constraints
by Spencer Hutchinson, Tianyi Chen, Mahnoosh Alizadeh
First submitted to arxiv on: 9 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The paper introduces Optimistically Safe OCO (OSOCO), an algorithm for online convex optimization under unknown linear constraints that can be static or stochastically time-varying. OSOCO achieves a regret of () and no constraint violation, improving on the previous best known (T^{2/3}) regret for static linear constraints. The algorithm also performs well in scenarios with stochastic time-varying constraints, offering O() regret and O() cumulative violation under more general convex constraints. Numerical results further demonstrate the effectiveness of OSOCO. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a problem where machines learn how to make good choices online while following certain rules that might change over time. It creates an algorithm called Optimistically Safe OCO (OSOCO) that can handle these changing rules and makes sure it follows them correctly. The new algorithm is better than what was known before, especially when the rules don’t change much. It also works well in cases where the rules do change a lot. The paper shows some examples to prove how well OSOCO really performs. |
Keywords
* Artificial intelligence * Optimization
