Summary of Adaptive Optimization For Prediction with Missing Data, by Dimitris Bertsimas et al.
Adaptive Optimization for Prediction with Missing Data
by Dimitris Bertsimas, Arthur Delarue, Jean Pauphilet
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 In a significant departure from traditional pipelines, researchers propose a novel approach to predictive modeling that tackles missing data by viewing it as a two-stage adaptive optimization problem. This new class of models, called adaptive linear regression models, allows the regression coefficients to adapt to the observed features, leading to improved out-of-sample accuracy in settings where data is not missing at random. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way to make predictions when some data is missing. Instead of using a usual pipeline approach that first fills in the gaps and then makes the prediction, this method views the problem as an optimization task that adapts to what’s available. This leads to better results, especially when the missing data is not randomly distributed. |
Keywords
* Artificial intelligence * Linear regression * Optimization * Regression
