Coupled Training with Privileged Information and Unlabeled Data
In many prediction problems, we have extra information during training (for example, measurements that are expensive or slow to collect) that will not be available when the model is deployed. A common strategy is to first train a model that uses all training information, then use its predictions on unlabeled examples to train a second model that only uses the inputs available at test time. However, when the extra training-only information is weak or noisy, this Two-Stage approach can mislead the deployment model and even hurt accuracy. We propose a joint training method that learns the two models together, so the deployment model can benefit from the extra information only when it actuall...
| Authors | Jiahao Shi, Omar Hagrass, Jason M. Klusowski |
|---|---|
| Primary category | stat.ML |
| Published | 22 May 2026 |
| Last updated at source | 7 August 2026 |
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