Summary of Safe Online Convex Optimization with Multi-point Feedback, by Spencer Hutchinson et al.
Safe Online Convex Optimization with Multi-Point Feedback
by Spencer Hutchinson, Mahnoosh Alizadeh
First submitted to arxiv on: 16 Jul 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 proposed algorithm leverages forward-difference gradient estimation, optimistic and pessimistic action sets to achieve sublinear regret and zero constraint violation in a multi-point feedback setting for safe online convex optimization. The algorithm achieves O(d√T) regret and zero constraint violation under the assumption of smooth and strongly convex constraint functions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes an algorithm that uses forward-difference gradient estimation and optimistic/pessimistic action sets to solve a safe online convex optimization problem with multi-point feedback. The goal is to achieve sublinear regret and zero constraint violation, which is important for real-world applications where safety requirements are stringent. |
Keywords
* Artificial intelligence * Optimization
