Deep Multitask Learning for Mixed-Type Outcomes with Shared Sparsity
Most existing multitask learning approaches are limited by their reliance on task-specific loss functions tailored to the scale and type of each outcome. When outcomes differ across tasks, these losses are generally not directly comparable, which makes it difficult to formulate a unified objective and may limit information sharing across tasks. We propose a multitask transformation framework in which task-specific responses may differ through unknown monotone transformations. Motivated by high-dimensional biological applications in which the predictor dimension may diverge with the sample size while only a common subset of predictors is informative, we consider shared sparsity across task...
| Authors | Huichao Li, Tong Wang, Sanguo Zhang et al. |
|---|---|
| Primary category | stat.ML |
| Published | 1 July 2026 |
| Last updated at source | 1 July 2026 |
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