MORE-PLR: multi-output regression employed for partial label ranking

The paper introduces MORE-PLR, a method that tackles the partial label ranking problem by employing multi-output regression. It uses an encoder to transform incomplete rankings with ties into regression targets during training, and applies post‑hoc layers during inference to convert regression outputs into bucket orders. Experiments show that this framework competes with state‑of‑the‑art partial label ranking methods.

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