Summary of Reconciling Model Multiplicity For Downstream Decision Making, by Ally Yalei Du et al.
Reconciling Model Multiplicity for Downstream Decision Making
by Ally Yalei Du, Dung Daniel Ngo, Zhiwei Steven Wu
First submitted to arxiv on: 30 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 The abstract presents a framework for model multiplicity in downstream decision-making, where multiple predictive models of equal accuracy disagree on the best response action. The authors show that even when individual predictions are similar, induced best-response actions can still differ significantly. To address this issue, they propose an algorithm that calibrates predictive models to agree with both the downstream loss function and individual probability prediction. The framework leverages multi-calibration tools and ensures the updated models are indistinguishable from the true probability distribution. Experimental results demonstrate improved downstream decision-making losses and agreement on best-response actions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers tackle a problem where multiple predictive models agree on predictions but not on what action to take next. They show that even when individual predictions are similar, different actions can still be chosen. To solve this issue, they develop an algorithm that adjusts the models to agree with both the task and individual predictions. This helps ensure that the best response is chosen most of the time. |
Keywords
» Artificial intelligence » Loss function » Probability
