The Analysis of Permutations

Cited by (732)

Items 301–400 of 732

1. Supervised Clustering of Label Ranking Data
   Mihajlo Grbovic, Nemanja Djuric, Slobodan Vučetić · 2012 · SIAM International Conference on Data Mining
2. A Bayesian Mallows approach to nontransitive pair comparison data: How human are sounds?
   Marta Crispino, Elja Arjas, Valeria Vitelli, Natasha Barrett, Arnoldo Frigessi · 2019 · The Annals of Applied Statistics
3. Partial Rank Data with the hyper2 Package: Likelihood Functions for Generalized Bradley-Terry Models
   Robin,K.,S. Hankin · 2017 · The R Journal
4. Algorithm 989
   Ekhiñe Irurozki, Josu Ceberio, Josean Santamaria, Roberto Santana, Alexander Mendiburu · 2018 · ACM Transactions on Mathematical Software
5. A Multimodal Machine Learning Framework for Teacher Vocal Delivery Evaluation
   Hang Li, Yu Kang, Hao Yang, Wenbiao Ding, Zhongqin Wu, Zitao Liu · 2021 · Lecture notes in computer science
6. Optimizing Immersive Video Coding Configurations Using Deep Learning: A Case Study on TMIV
   Chih-Fan Hsu, Tse-Hou Hung, Cheng-Hsin Hsu · 2022 · ACM Transactions on Multimedia Computing Communications and Applications
7. Stochastic Retrieval-Conditioned Reranking
   Hamed Zamani, Michael Bendersky, Donald Metzler, Honglei Zhuang, Xuanhui Wang · 2022 · ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR)
8. Probabilistic Models over Ordered Partitions with Applications in Document Ranking and Collaborative Filtering
   Truyen Tran, Dinh Phung, Svetha Venkatesh · 2011 · SIAM International Conference on Data Mining
9. Resource Allocation in Public Agencies: Experimental Evidence
   Juan-Camilo Cárdenas, Rajiv Sethi · 2010 · Journal of Public Economic Theory
10. Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach
    Adil El Mesaoudi-Paul, Dimitri Weiß, Viktor Bengs, Eyke Hüllermeier, Kevin Tierney · 2020 · Lecture notes in computer science
11. TrajFlow: Multi-modal Motion Prediction via Flow Matching
    Qi Yan, Brian Zhang, Yutong Zhang, Daniel Z. Yang, Joshua White, Di Chen, Jiachao Liu, Langechuan Liu, Binnan Zhuang, Shaoshuai Shi et al. · 2025 · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
12. Emulation of Utility Functions Over a Set of Permutations: Sequencing Reliability Growth Tasks
    Kevin J. Wilson, Daniel A. Henderson, John Quigley · 2017 · Technometrics
13. Gambler’s Ruin and the ICM
    Persi W. Diaconis, S. N. Ethier · 2022 · Statistical Science
14. Ranking-based second stage in data envelopment analysis: An application to research efficiency in higher education
    Vladimír Holý · 2024 · Operations Research Perspectives
15. Learning to rank quantum circuits for hardware-optimized performance enhancement
    Gavin S. Hartnett, Aaron Barbosa, Pranav Mundada, Michael Hush, Michael J. Biercuk, Yuval Baum · 2024 · Quantum
16. Towards Human-Centered Construction Robotics: A Reinforcement Learning-Driven Companion Robot for Contextually Assisting Carpentry Workers
    Yuning Wu, Jiaying Wei, Jean Oh, Daniel Cardoso Llach · 2024 · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
17. Efficient Sampling for Bipartite Matching Problems
    Maksims Volkovs, Richard S. Zemel · 2012
18. Learning Mixtures of Random Utility Models with Features from Incomplete Preferences
    Zhibing Zhao, Ao Liu, Lirong Xia · 2022 · International Joint Conference on Artificial Intelligence
19. An Offline Metric for the Debiasedness of Click Models
    Romain Deffayet, Philipp Hager, Jean-Michel Renders, Maarten de Rijke · 2023 · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)
20. Overcoming Diverse Undesired Effects in Recommender Systems: A Deontological Approach
    Paula Gómez Duran, Pere Gilabert, Santi Seguí, Jordi M. Vitria · 2024 · ACM Transactions on Intelligent Systems and Technology
21. An Estimation of Generalized Bradley-Terry Models Based on the em Algorithm
    Yu Fujimoto, Hideitsu Hino, Noboru Murata · 2011 · Neural Computation
22. Statistical Response Method and Learning Data Acquisition using Gamified Crowdsourcing for a Non-task-oriented Dialogue Agent
    Michimasa Inaba, Naoyuki Iwata, Fujio Toriumi, Takatsugu Hirayama, Yu Enokibori, Keníchi Takahashi, Kenji Mase · 2015 · Lecture notes in computer science
23. Bayesian Non-Parametric Clustering of Ranking Data
    Marina Meilă, Harr Chen · 2016 · IEEE Transactions on Pattern Analysis and Machine Intelligence
24. A computationally intensive ranking system for paired comparison data
    David Beaudoin, Tim B. Swartz · 2018 · Operations Research Perspectives
25. New order-statistics-based ranking models and faster computation of outcome probabilities
    Rose Baker · 2019 · IMA Journal of Management Mathematics
26. Submodular Order Functions and Assortment Optimization
    Rajan Udwani · 2024 · Management Science
27. An Optimal Greedy Heuristic with Minimal Learning Regret for the Markov Chain Choice Model
    Guillermo Gallego, Wentao Lu · 2021 · SSRN Electronic Journal
28. Ex-ante priority setting in crop breeding: Towards inclusive innovation for impact
    Martina Occelli, E. Compaoré, David Brown, Fanta Barry, Léandre Poda, Fousséni Traore, Antoine Waongo, D. Rubin, Hale Tufan · 2024 · Agricultural Systems
29. Uncertainty Quantification and Decomposition for LLM-based Recommendation
    Wonbin Kweon, Sanghwan Jang, SeongKu Kang, Hwanjo Yu · 2025 · International World Wide Web Conference (WWW)
30. UnitedMet harnesses RNA–metabolite covariation to impute metabolite levels in clinical samples
    Amy Xie, Wesley Tansey, Ed Reznik · 2025 · Nature Cancer
31. Synergistic and combinatorial optimization of finite element models for monitored buildings
    Gaetano Miraglia, Erica Lenticchia, Rosario Ceravolo, Raimondo Betti · 2019 · Structural Control and Health Monitoring
32. Preference elicitation and robust winner determination for single- and multi-winner social choice
    Tyler Lu, Craig E. Boutilier · 2019 · Artificial Intelligence
33. Multi-prototype label ranking with novel pairwise-to-total-rank aggregation
    Mihajlo Grbovic, Nemanja Djuric, Slobodan Vučetić · 2013
34. Comparative Judgement for Linking Two Existing Scales
    Tom Benton · 2021 · Frontiers in Education
35. On Top-$k$ Selection From $m$-Wise Partial Rankings via Borda Counting
    Wenjing Chen, Ruida Zhou, Chao Tian, Cong Shen · 2022 · IEEE Transactions on Signal Processing
36. A Kemeny Distance-Based Robust Fuzzy Clustering for Preference Data
    Pierpaolo D’Urso, Vincenzina Vitale · 2022 · Journal of Classification
37. Learning to Design Fair and Private Voting Rules
    Farhad Mohsin, Ao Liu, Pin‐Yu Chen, Francesca Rossi, Lirong Xia · 2022 · Journal of Artificial Intelligence Research
38. Probability Estimation for Multi-class Classification Based on Label Ranking
    Weiwei Cheng, Eyke Hüllermeier · 2012 · Lecture notes in computer science
39. Interpretation of Average Ranks
    Robert J. Henery · 1986 · Biometrika
40. Active top-K ranking from noisy comparisons
    Soheil Mohajer, Changho Suh · 2016 · Allerton Conference on Communication, Control, and Computing
41. Disentangling Wine Judges’ Consensus, Idiosyncratic, and Random Expressions of Quality or Preference
    Jeffrey C. Bodington · 2017 · Journal of Wine Economics
42. Statistical Computational Learning
    Antoine Cornuéjols, Frédéric Koriche, Richard Nock · 2020 · Springer eBooks
43. Interactive Text Ranking with Bayesian Optimization: A Case Study on Community QA and Summarization
    Edwin Simpson, Yang Gao, Iryna Gurevych · 2020 · Transactions of the Association for Computational Linguistics
44. Forecasting Tennis Match Results Using the Bradley-Terry Model
    Aisha Fayomi, Rizwana Majeed, Ali Algarni, Sohail Akhtar, Farrukh Jamal, Jamal Abdul Nasir · 2022 · International Journal of Photoenergy
45. Evaluating the Robustness of Click Models to Policy Distributional Shift
    Romain Deffayet, Jean-Michel Renders, Maarten de Rijke · 2022 · ACM Transactions on Information Systems
46. Querywise Fair Learning to Rank through Multi-Objective Optimization
    Debabrata Mahapatra, Chaosheng Dong, Michinari Momma · 2023 · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
47. Scalable Bayesian preference learning for crowds
    Edwin Simpson, Iryna Gurevych · 2020 · Machine Learning
48. Individually Fair Rankings
    Amanda Bower, Hamid Eftekhari, Mikhail Yurochkin, Yuekai Sun · 2021 · International Conference on Learning Representations
49. Modeling Label Ambiguity for Neural List-Wise Learning to Rank
    Rolf Jagerman, Julia Kiseleva, Maarten de Rijke · 2017 · arXiv (Cornell University)
50. From Hand-Perspective Visual Information to Grasp Type Probabilities: Deep Learning via Ranking Labels
    Mo Han, Sezen Yağmur Günay, İlkay Yıldız, Paolo Bonato, Cagdas Denizel Onal, Taşkın Padır, Gunar Schirner, Erdo{ğ}muş, Deniz · 2021 · arXiv (Cornell University)
51. Strategy for Boosting Pair Comparison and Improving Quality Assessment Accuracy
    Suiyi Ling, Jing Li, Anne-Flore Perrin, Zhi Li, Lukáš Krasula, Patrick Le Callet · 2020 · arXiv (Cornell University)
52. Multi-FR: A Multi-Objective Optimization Method for Achieving Two-sided Fairness in E-commerce Recommendation.
    Haolun Wu, Chen Ma, Bhaskar Mitra, Fernando Díaz, Xue Liu · 2021 · arXiv (Cornell University)
53. Constructive Preference Elicitation Over Hybrid Combinatorial Spaces
    Paolo Dragone, Stefano Teso, Andrea Passerini · 2018 · Proceedings of the AAAI Conference on Artificial Intelligence
54. Change Point Detection in Pairwise Comparison Data with Covariates
    Yi Han, Thomas C. M. Lee · 2025 · Journal of Computational and Graphical Statistics
55. Approximate Condorcet Partitioning: Solving large-scale rank aggregation problems
    Sina Akbari, Adolfo R. Escobedo · 2023 · Computers & Operations Research
56. Scalable and Provably Fair Exposure Control for Large-Scale Recommender Systems
    Riku Togashi, Kenshi Abe, Yuta Saito · 2024 · International World Wide Web Conference (WWW)
57. Application of Fuzzy Ordinal Peer Assessment in Formative Evaluation
    N. Capuano, F. Orciuoli · 2017 · International Conference on P2P, Parallel, Grid, Cloud and Internet Computing
58. Stagewise learning for noisy k-ary preferences
    Yuangang Pan, Bo Han, Ivor Wai-Hung Tsang · 2018 · Machine Learning
59. Group decision making under uncertain preferences: powered by AI, empowered by AI
    Lirong Xia · 2022 · Annals of the New York Academy of Sciences
60. Geometry of EM and related iterative algorithms
    Hideitsu Hino, Shotaro Akaho, Noboru Murata · 2022 · Information Geometry
61. An in-depth study on adversarial learning-to-rank
    Hai-Tao Yu, Rajesh Piryani, Adam Jatowt, Ryo Inagaki, Hideo Joho, Kyoung-Sook Kim · 2023 · Information Retrieval
62. A theorem on CUB models for rank data
    Maria Iannario, Domenico Piccolo · 2014 · Statistics & Probability Letters
63. Modelling human preferences for ranking and collaborative filtering: a probabilistic ordered partition approach
    Truyen Tran, Dinh Phung, Svetha Venkatesh · 2015 · Knowledge and Information Systems
64. Neural Choice by Elimination via Highway Networks
    Truyen Tran, Dinh Phung, Svetha Venkatesh · 2016 · Lecture notes in computer science
65. A Note on the Boltzmann Distribution and the Linear Ordering Problem
    Josu Ceberio, Alexander Mendiburu, Jose Antonio Lozano · 2016 · Lecture notes in computer science
66. Maximum likelihood estimation for incomplete multinomial data via the weaver algorithm
    Fanghu Dong, Guosheng Yin · 2017 · Statistics and Computing
67. Data mining-based algorithm for assortment planning
    Praveen Ranjan Srivastava, Satyendra Kumar Sharma, Simran Preet Kaur · 2020 · Journal of Management Analytics
68. Bayesian analysis of ranking data with the Extended Plackett–Luce model
    Cristina Mollica, Luca Tardella · 2020 · Statistical Methods & Applications
69. Estimation of Skill Distributions
    Ali Jadbabaie, Anuran Makur, Devavrat Shah B · 2024 · IEEE Transactions on Information Theory
70. Estimating the number of clusters in a ranking data context
    Wilson Calmon, Mariana Albi de Oliveira Souza · 2020 · Information Sciences
71. Modelling Time-Varying Rankings with Autoregressive and Score-Driven Dynamics
    Vladimír Holý, Jan Zouhar · 2022 · Journal of the Royal Statistical Society Series C (Applied Statistics)
72. Rank‐based Bayesian variable selection for genome‐wide transcriptomic analyses
    Emilie Eliseussen, Thomas Fleischer, Valeria Vitelli · 2022 · Statistics in Medicine
73. Learning context-dependent choice functions
    Karlson Pfannschmidt, Pritha Gupta, Björn Haddenhorst, Eyke Hüllermeier · 2021 · International Journal of Approximate Reasoning
74. Diagnostic Evaluation of Policy-Gradient-Based Ranking
    Hai-Tao Yu, Degen Huang, Fuji Ren, Lishuang Li · 2021 · Electronics
75. Assortment Optimization under a Random Swap based Distribution over Permutations Model
    Antoine Désir, Vineet Goyal, Danny Segev · 2016 · ACM Conference on Economics and Computation (EC)
76. Rating, ranking or both? A joint application of two probabilistic models for the measurement of values
    Debora de Chiusole, Luca Stefanutti · 2011 · Padua Research Archive (University of Padova)
77. Optimizing Automatic Evaluation of Machine Translation with the ListMLE Approach
    Maoxi Li, Mingwen Wang · 2018 · ACM Transactions on Asian and Low-Resource Language Information Processing
78. Minimizing Time-to-Rank: A Learning and Recommendation Approach
    Haoming Li, Sujoy Sikdar, Rohit Vaish, Junming Wang, Lirong Xia, Chaonan Ye · 2019 · International Joint Conference on Artificial Intelligence
79. Scalable Bayesian nonparametric regression via a Plackett-Luce model for conditional ranks
    Tristan Gray-Davies, Chris C. Holmes, François Caron · 2016 · Electronic Journal of Statistics
80. A Grouped Ranking Model for Item Preference Parameter
    Hideitsu Hino, Yu Fujimoto, Noboru Murata · 2010 · Neural Computation
81. Conjugacy Class Prior Distributions on Metric-Based Ranking Models
    Jayanti Gupta, Paul Damien · 2002 · Journal of the Royal Statistical Society Series B (Statistical Methodology)
82. Aspects of two group concordance
    Michael A. Fligner, Joseph S. Verducci · 1987 · Communication in Statistics- Theory and Methods
83. Label Ranking mit probabilistischen Modellen
    Weiwei Cheng · 2012 · open_UMR Marburg DSpace 10.0 (Philipps-Universität Marburg)
84. Optimal Learning of Mallows Block Model
    Róbert Busa‐Fekete, Dimitris A. Fotakis, Balázs Szörényi, Manolis Zampetakis · 2019 · arXiv (Cornell University)
85. Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model
    Jiaqi Ma, Xinyang Yi, Tang, Weijing, Zhao, Zhe, Lichan Hong, Ed H., Qiaozhu Mei · 2020 · arXiv (Cornell University)
86. Revenue Management with Product Retirement and Customer Selection
    Adam N. Elmachtoub, Vineet Goyal, Roger Lederman, Harsh Sheth · 2022 · SSRN Electronic Journal
87. Spectral Ranking Inferences based on General Multiway Comparisons
    Jianqing Fan A B, Zhipeng Lou, Weichen Wang, Mengxin Yu · 2023 · SSRN Electronic Journal
88. A Survey of Progress in LLM Alignment From the Perspective of Reward Design
    Miaomiao Ji, Yanqiu Wu, Zhibin Wu, Shoujin Wang, Jian Yang, Mark Dras, Usman Naseem · 2026 · IEEE Transactions on Artificial Intelligence
89. Modelling and Analysis of Rank Ordered Data with Ties via a Generalized Plackett-Luce Model
    Daniel A. Henderson · 2024 · Bayesian Analysis
90. Probabilistic models over weighted orderings: fixed-parameter tractable variable elimination
    Thomas Lukasiewicz, María Vanina Martínez, David Poole, Gerardo I. Simari · 2016 · Archive of Doctoral Theses and Digital Collections (University of Calabria)
91. Blind Men and The Elephant: Thurstonian Pairwise Preference for Ranking in Crowdsourcing
    Xiaolong Wang, Jingjing Wang, Jie Luo, ChengXiang Zhai, Yi Chang · 2016 · Proceedings
92. Sample complexity of rank regression using pairwise comparisons
    Berkan Kadıoğlu, Peng Tian, Jennifer Dy, Deniz Erdogmus B, Stratis Ioannidis · 2022 · Pattern Recognition
93. Social order statistics models for ranking data with analysis of preferences in social networks
    Jiaqi Gu, Philip L. H. Yu · 2023 · The Annals of Applied Statistics
94. A state-space perspective on modelling and inference for online skill rating
    Samuel Duffield, Sam Power, Lorenzo Rimella · 2024 · Journal of the Royal Statistical Society Series C (Applied Statistics)
95. Use of Bayesian probabilistic model approach in common bean varietal recommendation
    Isabela R. Miranda, Kaio Olímpio das Graças Dias, José Domingos Pereira Júnior, Pedro Crescêncio Souza Carneiro, J.E.S. Carneiro, Vinícius Quintão Carneiro, Elaine Aparecida de Souza, Leonardo Cunha Melo, H. S. Pereira, Rogério Faria Vieira et al. · 2024 · Crop Science
96. Evaluating medical AI systems in dermatology under uncertain ground truth
    David Stutz, Ali Taylan Cemgil, Abhijit Guha Roy, Tatiana Matejovicova, Melih Barsbey, Patricia Strachan, Mike Schaekermann, Jan Freyberg, Rajeev Vijay Rikhye, Beverly Freeman et al. · 2025 · Medical Image Analysis
97. Statistical Inference on Random Structures
    Villő Csiszár, Lídia Rejtö, Gábor Tusnády · 2008 · Bolyai Society mathematical studies
98. Reconciling Real Scores with Binary Comparisons: A New Logistic Based Model for Ranking
    Nir Ailon · 2008 · neural information processing systems
99. A generative model for rank data based on sorting algorithm
    Christophe Biernacki A, Julien Jacques · 2009
100. Revealing Subgroup Structure in Ranked Data Using a Bayesian WAND
     Stephen R. Johnson, Daniel A. Henderson, Richard J. Boys · 2019 · Journal of the American Statistical Association