Generative Score Inference for Multimodal Data
Accurate uncertainty quantification is crucial for making reliable decisions in various supervised learning scenarios, particularly when dealing with complex, multimodal data such as images and text. Current approaches often face notable limitations, including rigid assumptions and limited generalizability, constraining their effectiveness across diverse supervised learning tasks. To overcome these limitations, we introduce Generative Score Inference (GSI), a flexible inference framework capable of constructing statistically valid and informative prediction and confidence sets across a wide range of multimodal learning problems. GSI utilizes synthetic samples generated by deep generative ...
| Authors | Xinyu Tian, Xiaotong Shen |
|---|---|
| Primary category | stat.ML |
| Published | 27 March 2026 |
| Last updated at source | 27 March 2026 |
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