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DF2: Distribution-Free Decision-Focused Learning

Created by
  • Haebom

Author

Lingkai Kong, Wenhao Mu, Jiaming Cui, Yuchen Zhuang, B. Aditya Prakash, Bo Dai, Chao Zhang

Outline

In this paper, we present DF2, a distribution-free decision-driven learning method, to address three major bottlenecks (model mismatch error, sample mean approximation error, and gradient approximation error) of recently emerged decision-driven learning (DFL) as a powerful approach to prediction-optimization problems. While traditional DFL relies on precise model assumptions of task-specific predictors, DF2 directly learns the expectation optimization function during training. It efficiently learns the function in a data-driven manner by employing an attention-based model architecture inspired by distribution-based parameterization of the expectation objective function. We demonstrate the effectiveness of DF2 through evaluations on two synthetic and three real-world problems.

Takeaways, Limitations

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Takeaways:
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We present a novel method (DF2) that effectively alleviates the main bottlenecks of DFL: model mismatch error, sample mean approximation error, and gradient approximation error.
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Adopt a data-driven approach to directly learn the expectation optimization function without making precise model assumptions about the predictor for a specific task.
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Achieving efficient learning through attention-based model architecture.
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Demonstrating the superiority of DF2 through experiments on synthetic and real-world problems.
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Reproducibility and extensibility through open code.
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Limitations:
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The scope of the presented experiments is limited (two synthetic problems and three real problems). Additional experiments on more diverse types of problems are needed.
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The performance of DF2 may vary depending on specific problem types or data distributions. Further analysis is needed on its robustness under various conditions.
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There is a lack of analysis on the computational complexity and training time of attention-based models. Further research is needed on more efficient training methods.
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