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.
arXiv:2607. 08103v1 Announce Type: new Abstract: Rank estimation under label noise poses a fundamental challenge, as ordinal annotations often exhibit structured uncertainty rather than simple label corruption.
arXiv:2606. 05308v1 Announce Type: new Abstract: With PRECISE, we extended Prediction-Powered Inference to produce bias-corrected estimates of ranking evaluation metrics by combining a small human-labeled set with a large LLM-judged set.
arXiv:2609.12915v1 Announce Type: new Abstract: Large-scale multi-label text classification assigns a small subset of relevant labels to each document from a vocabulary containing thousands or tens o...
arXiv:2604. 06614v2 Announce Type: replace-cross Abstract: Prompt learning has gained significant attention as a parameter-efficient approach for adapting large pre-trained vision-language models to downstream tasks.
arXiv:2609.36740v1 Announce Type: new Abstract: Many recommender systems such as for e-commerce and news platforms aim to provide users with rankings they are likely to interact with. Off-Policy Lear...
arXiv:2607. 23575v1 Announce Type: cross Abstract: Ordinal regression is widely used in scenarios where labels are discrete yet inherently ordered.
arXiv:2606. 04387v1 Announce Type: cross Abstract: Sales lead conversion in high-stakes domains (e.
arXiv:2601. 21816v2 Announce Type: replace Abstract: Evaluating the performance of large language models (LLMs) from human preference data is crucial for obtaining LLM leaderboards.
The paper introduces MoPLEx, an expectation‑maximization algorithm for learning mixtures of Plackett‑Luce models from multi‑way ranking data. It augments rankings with synthetic responses from a base language model and uses a gradient‑based estimation to reduce inference cost, enabling efficient fitting of large‑scale models. Experiments show the method achieves low probability estimation error and improves clustering and ranking accuracy by 43.7% and 15.2% over baselines.
arXiv:2604.24952v2 Announce Type: replace Abstract: Human visual preferences are inherently multi-dimensional, encompassing aesthetics, detail fidelity, and semantic alignment. However, existing data...
arXiv:2506. 06989v3 Announce Type: replace-cross Abstract: Learning-to-rank (LTR) systems commonly depend on implicit feedback, such as user clicks, because it is easy to collect and can serve as a valuable signal of user preferences.
arXiv:2411. 11436v2 Announce Type: replace-cross Abstract: In this paper, we address the problem of feature selection in the context of multi-label learning, by using a new estimator based on implicit regularization and label embedding.