arXiv:2608. 02081v1 Announce Type: new Abstract: In this paper, we study prediction problems for paired comparison data, for example, predicting the win probability between two unmatched players and ranking all the players according to the order of their strengths by using win probability data between two matched players.
arXiv:2501. 07437v3 Announce Type: replace-cross Abstract: Most statistical models for pairwise comparisons, including the Bradley-Terry (BT) and Thurstone models and many extensions, make a relatively strong assumption of stochastic transitivity.
arXiv:2510. 20454v2 Announce Type: replace Abstract: Intransitive player dominance, where player A beats B, B beats C, but C beats A, is common in competitive tennis.
arXiv:2505. 23437v2 Announce Type: replace-cross Abstract: Ranking systems influence decision-making in high-stakes domains like health, education, and employment, where they can have substantial economic and social impacts.
arXiv:2604. 17805v2 Announce Type: replace-cross Abstract: Pairwise ranking systems based on Maximum Likelihood Estimation (MLE), such as the Bradley-Terry model, are widely used to aggregate preferences from pairwise comparisons.
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.
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:2609.07617v1 Announce Type: new Abstract: With the rise of live sports betting in recent years, tennis forecasting has expanded from pre-match prediction to models that update win probabilities...
arXiv:2608.25200v2 Announce Type: replace-cross Abstract: We study learning a mixture of $k$ Plackett-Luce models from multi-way ranking responses from annotators that may represent heterogeneous und...
arXiv:2608. 03437v1 Announce Type: cross Abstract: While human evaluation is the gold standard in many NLP tasks, it suffers from prohibitive costs and poor scalability.
arXiv:2606. 06043v1 Announce Type: cross Abstract: Follow-the-regularized-leader framework has shown effectiveness and flexibility in online learning problems, where the choice of learning rates are known to be crucial.
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: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...