CAREER: Integrating Conformal Prediction into Machine Learning for Provably Safe Deploymen — NSF Award to Washington State Univers
Advances in machine learning (ML) have opened up new possibilities for accurate predictions in various fields. To ensure the reliability of these predictions, especially in high-stakes decision-making scenarios, theory and tools need to be developed that provide confidence intervals. This research project aims to creat
| Award title | CAREER: Integrating Conformal Prediction into Machine Learning for Provably Safe Deploymen |
|---|---|
| Award ID | 2443828 |
| Awardee | Washington State University |
| City | PULLMAN |
| State | WA |
| Amount obligated | $317,381 |
| Principal investigator | Yan Yan |
| Program | Robust Intelligence |
| Start date | 06/15/2025 |
| Abstract | Advances in machine learning (ML) have opened up new possibilities for accurate predictions in various fields. To ensure the reliability of these predictions, especially in high-stakes decision-making scenarios, theory and tools need to be developed that provide confidence intervals. This research project aims to create innovative methods for quantifying uncertainty in complex systems, allowing for more informed and confident decision-making. One key aspect of this work is designing efficient co |
| Source | NSF Awards |
$799/mo
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