Summary of Model Ensembling For Constrained Optimization, by Ira Globus-harris et al.
Model Ensembling for Constrained Optimization
by Ira Globus-Harris, Varun Gupta, Michael Kearns, Aaron Roth
First submitted to arxiv on: 27 May 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 explores ways to combine machine learning models for multidimensional output predictions, with the goal of optimizing a linear objective under specified constraints. The authors investigate ensembling models that map states to real-valued predictions and develop two provably efficient and convergent algorithms: one requiring white-box models and another using black box policies. These methods leverage multicalibration techniques to improve performance and provide guarantees on convergence and utility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about combining machine learning models to make better predictions. It’s like a team of experts working together to solve a problem. Right now, we can combine models that predict things like whether someone will click on an ad or not. But what if we want to combine models that predict many different things at once? And what if we want to use these combined predictions to optimize something, like finding the best way to schedule a fleet of trucks? The paper shows two ways to do this: one that needs lots of information about how each model works and another that only needs some basic information. Both methods are tested in a controlled experiment to see which one performs better. |
Keywords
» Artificial intelligence » Machine learning
