Collaborative Research: CIF: Small: New Theory, Algorithms and Applications for Large-Scal — NSF Award to Johns Hopkins University
In recent years, the world has witnessed significant progress in optimization for emerging fields, including meta-learning, fine-tuning, automated hyperparameter selection, continual learning, fair batch selection, adversarial learning, and artificial intelligence (AI)-aware communication networks. Problems arising fro
| Award title | Collaborative Research: CIF: Small: New Theory, Algorithms and Applications for Large-Scal |
|---|---|
| Award ID | 2626366 |
| Awardee | Johns Hopkins University |
| City | BALTIMORE |
| State | MD |
| Amount obligated | $188,168 |
| Principal investigator | Shiqian Ma |
| Program | Comm & Information Foundations |
| Start date | 07/01/2026 |
| Abstract | In recent years, the world has witnessed significant progress in optimization for emerging fields, including meta-learning, fine-tuning, automated hyperparameter selection, continual learning, fair batch selection, adversarial learning, and artificial intelligence (AI)-aware communication networks. Problems arising from these fields often exhibit a common nested optimization structure, which has motivated the study of bilevel optimization. However, there are many theoretical and computational ch |
| Source | NSF Awards |
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