Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization

Understand

Creating impact in real-world settings requires artificial intelligence techniques to span the full pipeline from data, to predictive models, to decisions.

• These components are typically approached separately: a machine learning model is first trained via a measure of predictive accuracy, and then its predictions are used as input into an optimization algorithm which produces a decision.
• However, the loss function used to train the model may easily be misaligned with the end goal, which is to make the best decisions possible.
• Hand-tuning the loss function to align with optimization is a difficult and error-prone process (which is often skipped entirely).