Bibliographic record
Abstract
While multitask learning has been extensively studied, most existing methods rely on linear models (e.g. linear regression, logistic regression), which may fail in dealing with more general (nonlinear) problems. In this paper, we present a new approach that combines dictionary learning with gradient boosting to achieve multitask learning with general (nonlinear) basis functions. Specifically, for each task we learn a sparse representation in a nonlinear dictionary that is shared across the set of tasks. Each atom of the dictionary is a nonlinear feature mapping of the original input space, learned in function space by gradient boosting. The resulting model is a hierarchical ensemble where the top layer of the hierarchy is the task-specific sparse coefficients and the bottom layer is the boosted models common to all tasks. The proposed method takes the advantages of both dictionary learning and boosting for multitask learning: knowledge across tasks can be shared via the dictionary, and flexibility and generalization performance are guaranteed by boosting. More important, this general framework can be used to adapt any learning algorithm to (nonlinear) multitask learning. Experimental results on both synthetic and benchmark real-world datasets confirm the effectiveness of the proposed approach for multitask learning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".