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  "Description": "Fits regularised multi-task learning models where\nrelationships between tasks are controlled via orthogonality or\ndisjoint-support constraints. Supports regression, binary\nclassification, and censored survival data. In survival mode,\ntime-to-event outcomes are converted into binary labels at\nuser-defined thresholds, enabling the discovery of features\nwith time-varying effects that standard proportional-hazards\nmodels cannot detect. Implements the penalty described in\nVervier et al. (2014) <https://hal.science/hal-00985654>.",
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      "title": "Classification Accuracy for Multi-Task Predictions",
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      "title": "Project a Vector onto Non-Negative Non-Increasing Space",
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      "page": "orthoMTL",
      "title": "Multi-task learning with orthogonal constraints",
      "topics": [
        "orthoMTL"
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      "title": "Bootstrap Coefficient Comparison Plot",
      "topics": [
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      "title": "Task Correlation Map for orthoMTL",
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