Summary of Learning Joint Models Of Prediction and Optimization, by James Kotary et al.
Learning Joint Models of Prediction and Optimization
by James Kotary, Vincenzo Di Vito, Jacob Cristopher, Pascal Van Hentenryck, Ferdinando Fioretto
First submitted to arxiv on: 7 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Predict-Then-Optimize framework combines machine learning models with optimization techniques to improve decision quality. Recent work has shown that end-to-end training can lead to significant improvements, but this approach requires substantial computational effort and problem-specific rules for backpropagation. This paper proposes an alternative method, Learning-to-Optimize, which learns optimal solutions directly from observable features using joint predictive models. The approach is generic and adaptable, leveraging existing techniques from the Learning-to-Optimize paradigm. Experimental results demonstrate the effectiveness of several methods in solving challenging Predict-Then-Optimize problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make better decisions by combining machine learning with optimization techniques. Right now, we have a way to do this that works well but requires a lot of computer power and special rules for each problem. The new method in this paper learns the best solution directly from available data using a combination of predictive models. This approach is flexible and can use existing methods to solve many different problems. The results show that this method can solve tough decision-making problems effectively. |
Keywords
» Artificial intelligence » Backpropagation » Machine learning » Optimization
