plackett-luce model

Provably Efficient Personalized Multi-Objective Bandits with Proactive Conversational Queries UAI 2026 Comparing Few to Rank Many: Active Human Preference Learning Using Randomized Frank-Wolfe Method ICML 2025 Finally Rank-Breaking Conquers MNL Bandits: Optimal and Efficient Algorithms for MNL Assortment ICLR 2025 Strong Preferences Affect the Robustness of Preference Models and Value Alignment ICLR 2025 When the Universe is Too Big: Bounding Consideration Probabilities for Plackett-Luce Rankings AISTATS 2025 Efficient and Accurate Learning of Mixtures of Plackett-Luce Models AAAI 2023 Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisons ICML 2023 Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness (Extended Abstract) IJCAI 2022 Learning Mixtures of Random Utility Models with Features from Incomplete Preferences IJCAI 2022 Deep Spectral Ranking AISTATS 2021 Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model AISTATS 2021 Monocular Depth Estimation via Listwise Ranking Using the Plackett-Luce Model CVPR 2021 Preference Elicitation as Average-Case Sorting AAAI 2021 Single Player Monte-Carlo Tree Search Based on the Plackett-Luce Model AAAI 2021 Fast and Accurate Ranking Regression AISTATS 2020 From PAC to Instance-Optimal Sample Complexity in the Plackett-Luce Model ICML 2020 Learning Rich Rankings NeurIPS 2020 Preselection Bandits ICML 2020 Fast and Stable Maximum Likelihood Estimation for Incomplete Multinomial Models ICML 2019 Learning Mixtures of Plackett-Luce Models from Structured Partial Orders NeurIPS 2019 Learning Plackett-Luce Mixtures from Partial Preferences AAAI 2019 Near-Neighbor Methods in Random Preference Completion AAAI 2019 PAC Battling Bandits in the Plackett-Luce Model ALT 2019 Variational Inference from Ranked Samples with Features ACML 2019 Composite Marginal Likelihood Methods for Random Utility Models ICML 2018