Summary of Locally Convex Global Loss Network For Decision-focused Learning, by Haeun Jeon et al.
Locally Convex Global Loss Network for Decision-Focused Learning
by Haeun Jeon, Hyunglip Bae, Minsu Park, Chanyeong Kim, Woo Chang Kim
First submitted to arxiv on: 4 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper proposes Locally Convex Global Loss Network (LCGLN), a global surrogate loss model that can be implemented in a general Decision-Focused Learning (DFL) paradigm. DFL integrates prediction and optimization by adapting the predictive model to give better decisions for the corresponding task. The authors address an inevitable challenge when computing the gradients of the optimal decision with respect to the parameters, which existing research typically copes with by smoothly reforming surrogate optimization or constructing surrogate loss functions that mimic task loss. However, these approaches are often restricted to specific optimization domains. LCGLN learns task loss via a partial input convex neural network, ensuring convexity for chosen inputs while preserving non-convex global structure for other inputs. This enables LCGLN to be used in general DFL settings without requiring specific parametric forms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LCGLN is a new model that helps make better decisions by predicting unknown parameters and optimizing choices. The authors were trying to figure out how to make this work better, especially when the problem is uncertain. They came up with a way to learn from the task at hand and adapt the predictive model to give more accurate decisions. This approach can be used in many different situations where decision-making is important. |
Keywords
* Artificial intelligence * Neural network * Optimization
