Summary of Scalable Kernel Inverse Optimization, by Youyuan Long et al.
Scalable Kernel Inverse Optimization
by Youyuan Long, Tolga Ok, Pedro Zattoni Scroccaro, Peyman Mohajerin Esfahani
First submitted to arxiv on: 31 Oct 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 The proposed paper extends the framework of Inverse Optimization to learn unknown objective functions from expert decision-makers by utilizing reproducing kernel Hilbert spaces. This enhances feature representation in an infinite-dimensional space, enabling more accurate modeling. The representer theorem is reformulated as a finite-dimensional convex optimization program, allowing for efficient training. To address scalability issues, the Sequential Selection Optimization algorithm is introduced to train the Kernel Inverse Optimization model. Experimental results demonstrate the generalization capabilities and effectiveness of the proposed method on MuJoCo benchmark tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how to learn from an expert’s decisions by looking at past data. They create a new way to represent features in a very high-dimensional space, which helps them better understand what the expert is trying to achieve. This lets them reformulate a problem as a smaller optimization program that can be solved efficiently. The authors also introduce a new algorithm to make this process faster and more practical. The results show that their approach works well on certain types of learning tasks. |
Keywords
* Artificial intelligence * Generalization * Optimization
