Summary of End-to-end Conformal Calibration For Optimization Under Uncertainty, by Christopher Yeh et al.
End-to-End Conformal Calibration for Optimization Under Uncertainty
by Christopher Yeh, Nicolas Christianson, Alan Wu, Adam Wierman, Yisong Yue
First submitted to arxiv on: 30 Sep 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 presents an end-to-end framework for learning uncertainty estimates in high-capacity prediction models like deep neural networks, focusing on conditional robust optimization. The framework provides robustness and calibration guarantees through conformal prediction, addressing the issue of multiple valid uncertainty estimates with different performance profiles in high-dimensional settings. Key components include partially input-convex neural networks representing arbitrary convex uncertainty sets and a two-stage estimate-then-optimize baseline for comparison. Applications showcased include energy storage arbitrage and portfolio optimization, where the approach consistently outperforms baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to make smart decisions when there’s uncertainty involved. It creates a system that can learn how uncertain something is and use that information to make better choices. This is important because high-tech models like deep neural networks are really good at making predictions, but they often don’t know how certain they are about those predictions. The paper shows how its approach can be used in real-world applications like choosing the best times to store energy or picking the right investments. |
Keywords
* Artificial intelligence * Optimization
