The Analysis of Permutations

Cited by (732)

Items 201–300 of 732

1. Which is the best experimental design in animal choice tests?
   Octavio Augusto Bruzzone, Juan C. Corley · 2011 · Animal Behaviour
2. Adversarial Top- $K$ Ranking
   Changho Suh, Vincent Y. F. Tan, Renbo Zhao · 2017 · IEEE Transactions on Information Theory
3. Learning and Decision-Making from Rank Data
   Lirong Xia · 2019 · Synthesis lectures on artificial intelligence and machine learning
4. Deterministic Evolution of Strength in Multiple Comparisons Models: Who is the Greatest Golfer?
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5. Mixture of Experts Modelling with Social Science Applications
   Isobel Claire Gormley, Thomas Brendan Murphy · 2011 · Wiley series in probability and statistics
6. Rank Centrality: Ranking from Pair-wise Comparisons
   Sahand N. Negahban, Sewoong Oh, Devavrat Shah B · 2012 · arXiv (Cornell University)
7. CRF framework for supervised preference aggregation
   Maksims Volkovs, Richard S. Zemel · 2013 · ACM International Conference on Information and Knowledge Management (CIKM)
8. FOPA-MC: fuzzy multi-criteria group decision making for peer assessment
   Nicola Capuano, Santi Caballé, Gennaro Percannella, Pierluigi Ritrovato · 2020 · Soft Computing
9. 1Compression in the Space of Permutations
   Da Wang, Arya Mazumdar, Gregory W. Wornell · 2014
10. Exploring the Validity of Comparative Judgement: Do Judges Attend to Construct-Irrelevant Features?
    Lucy C. Chambers, Euan Cunningham · 2022 · Frontiers in Education
11. Bayesian Analysis of Rank Data with Covariates and Heterogeneous Rankers
    Xinran Li, Dingdong Yi, Jun S. Liu · 2022 · Statistical Science
12. Rank‐based data synthesis of common bean on‐farm trials across four Central American countries
    David Brown, Sytze de Bruin, Kauê de Sousa, Amílcar Aguilar, Mirna Barrios, Néstor Chaves-Barrantes, Marvin Gómez, Juan Carlos Hernández, Lewis Machida, B. Madriz et al. · 2022 · Crop Science
13. Compression in the Space of Permutations
    Da Wang, Arya Mazumdar, Gregory W. Wornell · 2015 · IEEE Transactions on Information Theory
14. Modelling ranks using the inverse hypergeometric distribution
    Angela D’Elia · 2003 · Statistical Modelling
15. A Severity Score for Retinopathy of Prematurity
    Peng Tian, Yuan Guo, Jayashree Kalpathy–Cramer, Susan R. Ostmo, J. Peter Campbell, Michael F. Chiang, Jennifer Dy, Deniz Erdogmus B, Stratis Ioannidis · 2019 · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
16. An Extreme-Value Model for Predicting the Results of Horse Races
    Robert J. Henery · 1984 · Journal of the Royal Statistical Society Series C (Applied Statistics)
17. The Determination of the Optimum Position on a Ballot Paper
    GRAHAM J. G. UPTON, D. Brook · 1975 · Journal of the Royal Statistical Society Series C (Applied Statistics)
18. Normal Order Statistics as Permutation Probability Models
    B. R. Dansie · 1986 · Journal of the Royal Statistical Society Series C (Applied Statistics)
19. Citizen science informs demand‐driven breeding of opportunity crops
    Rachel C. Voss, Kauê de Sousa, Sognigbé N’Danikou, Abdul J. Shango, Lys Amavi Aglinglo, Marie‐Angélique Laporte, Eric C. Legba, Aristide Carlos Houdegbe, Danfing dit Youssouf Diarra, Aminata Dolo et al. · 2025 · Plants People Planet
20. Unbiased one-dimensional university ranking – application-based preference ordering
    András Telcs, Zsolt Tibor Kosztyán, Ádám Török · 2015 · Journal of Applied Statistics
21. Learning to rank under multiple annotators
    Ou Wu, Weiming Hu, Jun Tao Gao · 2011 · International Joint Conference on Artificial Intelligence
22. Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce Model
    Julian Lienen, Eyke Hüllermeier, Ralph Ewerth, Nils Nommensen · 2021 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
23. A review on instance ranking problems in statistical learning
    Tino Werner · 2021 · Machine Learning
24. Comparing Direct and Indirect Methods of Audio Quality Evaluation in Virtual Reality Scenes of Varying Complexity
    Thomas Robotham, Olli Rummukainen, Miriam Kurz, Marie Eckert, Emanuël A. P. Habets · 2022 · IEEE Transactions on Visualization and Computer Graphics
25. Ranking social emotions by learning listwise preference
    Qishen Wang, Ou Wu, Weiming Hu, Jinfeng Yang, Wanqing Li · 2011 · Asian Conference on Pattern Recognition
26. Clustering Ranked Preference Data Using Sociodemographic Covariates
    Isobel Claire Gormley, Thomas Brendan Murphy · 2010 · Emerald Group Publishing Limited eBooks
27. A Comparison Between Two Models for Predicting Ordering Probabilities in Multiple-Entry Competitions
    Victor S. Y. Lo, John Bacon‐Shone · 1994 · Journal of the Royal Statistical Society Series D (The Statistician)
28. Assortment Optimization Under the Mallows model
    Antoine Désir, Vineet Goyal, Srikanth Jagabathula, Danny Segev · 2016 · neural information processing systems
29. Ranking Models with Item Covariates
    Douglas E. Critchlow, Michael A. Fligner · 1993 · Lecture notes in statistics
30. Computing with Unreliable Resources: Design, Analysis and Algorithms
    Da Wang · 2014 · DSpace@MIT (Massachusetts Institute of Technology)
31. Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening
    Mohsen Ahmadi Fahandar, Eyke Hüllermeier, Inés Couso · 2017 · arXiv (Cornell University)
32. A Topic Modeling Approach to Ranking
    Weicong Ding, Prakash Ishwar, Venkatesh Saligrama · 2014 · arXiv (Cornell University)
33. Efficient Neural Causal Discovery without Acyclicity Constraints
    Phillip Lippe, Taco Cohen, Efstratios Gavves · 2021 · arXiv (Cornell University)
34. Ranking Inferences Based on the Top Choice of Multiway Comparisons
    Jianqing Fan A B, Zhipeng Lou, Weichen Wang, Mengxin Yu · 2024 · Journal of the American Statistical Association
35. Listwise Generative Retrieval Models via a Sequential Learning Process
    Yubao Tang, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Wei Chen, Xueqi Cheng · 2024 · ACM Transactions on Information Systems
36. Towards Fair RAG: On the Impact of Fair Ranking in Retrieval-Augmented Generation
    To Eun Kim, Fernando Díaz · 2025 · ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR)
37. Learning-to-Rank at the Speed of Sampling
    Harrie Oosterhuis · 2022 · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)
38. Optimal Sample Complexity of M-wise Data for Top-K Ranking
    Minje Jang, Sunghyun Kim, Changho Suh, Sewoong Oh · 2017 · neural information processing systems
39. A label ranking method based on Gaussian mixture model
    Yangming Zhou, Yangguang Liu, Xiao-Zhi Gao, Guoping Qiu · 2014 · Knowledge-Based Systems
40. Efficiency of Racing, Sports, and Lottery Betting Markets
    William T. Ziemba · 2008 · Elsevier eBooks
41. Constructive Preference Elicitation for Multiple Users with Setwise Max-margin
    Stefano Teso, Andrea Passerini, Paolo Viappiani · 2017 · Lecture notes in computer science
42. Latent Structure Models for Ranking Data
    Marcel A. Croon, Ruud Luijkx · 1993 · Lecture notes in statistics
43. Bidding for Talent: A Test of Conduct in a High-Wage Labor Market
    Nina Roussille, Benjamin Scuderi · 2023 · SSRN Electronic Journal
44. Catch Me if I Can: Detecting Strategic Behaviour in Peer Assessment
    Ivan Stelmakh, Nihar B. Shah, Aarti Singh · 2021 · Proceedings of the AAAI Conference on Artificial Intelligence
45. Towards explaining graph neural networks via preserving prediction ranking and structural dependency
    Youmin Zhang, William K. Cheung, Qun Liu, Guoyin Wang, Lili Yang, Li Liu · 2023 · Information Processing & Management
46. Exploring experience design features in the metaverse concerts: a mixed-method approach
    Jijun Chen, Yoon Joo Lee, Xinran You Lehto, Yue Zhu · 2025 · Information Technology & Tourism
47. On the Algorithmic Bias of Aligning Large Language Models with RLHF: Preference Collapse and Matching Regularization
    Jiancong Xiao, Ziniu Li, Xingyu Xie, Emily J. Getzen, Cong Fang, Qi Long, Weijie Su · 2025 · Journal of the American Statistical Association
48. Common Scale Valuations across Different Preference-Based Measures
    Mónica Hernández Alava, John Edward Brazier, Donna Rowen, Aki Tsuchiya · 2013 · Medical Decision Making
49. Plackett-Luce Regression Mixture Model for Heterogeneous Rankings
    Maksim Tkachenko, Hady Wirawan Lauw · 2016 · ACM International Conference on Information and Knowledge Management (CIKM)
50. Kernels of Mallows Models under the Hamming Distance for solving the Quadratic Assignment Problem
    Etor Arza, Aritz Pérez, Ekhiñe Irurozki, Josu Ceberio · 2020 · Swarm and Evolutionary Computation
51. Kernels of Mallows Models under the Hamming Distance for solving the Quadratic Assignment Problem
    Arza, E., Pérez, A., Irurozki, E., Ceberio, J. · 2020 · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)
52. Kantorovich Distances between Rankings with Applications to Rank Aggregation
    Stéphan Clémençon, Jérémie Jakubowicz · 2010 · Lecture notes in computer science
53. Robust Plackett–Luce model for k-ary crowdsourced preferences
    Bo Han, Yuangang Pan, Ivor Wai-Hung Tsang · 2017 · Machine Learning
54. The Limits of Maxing, Ranking, and Preference Learning
    Moein Falahatgar, Ayush Jain, Alon Orlitsky, Venkatadheeraj Pichapati, Vaishakh Ravindrakumar · 2018 · International Conference on Machine Learning
55. An Alternative Approach to the Analysis of Permutations
    G. M. Tallis, B. R. Dansie · 1983 · Journal of the Royal Statistical Society Series C (Applied Statistics)
56. An Extended Mallows Model for Ranked Data Aggregation
    Han Li, Minxuan Xu, Jun S. Liu, Xiaodan Fan · 2019 · Journal of the American Statistical Association
57. Near-Neighbor Methods in Random Preference Completion
    Ao Liu, Qiong Wu, Zhenming Liu, Lirong Xia · 2019 · Proceedings of the AAAI Conference on Artificial Intelligence
58. Efficiency and Performance Guarantees for Choice-Based Network Revenue Management Problems with Flexible Products
    Wang Chi Cheung · 2016 · SSRN Electronic Journal
59. Ranked Choice Voting and Minority Representation
    Gerdus Benadè, Ruth Krebs Buck, Moon Duchin, Dara Gold, Thomas Weighill · 2021 · SSRN Electronic Journal
60. On‐farm evaluation of cassava clones using the triadic comparison of technology options approach
    Ann Ritah Nanyonjo, Stephen Angudubo, Paula Iragaba, David Brown, Ephraim Nuwamanya, Williams Esuma, Alfred Ozimati, Enoch Wembabazi, Michael Kanaabi, Nicholas Muhumuza et al. · 2024 · Crop Science
61. Sparse choice models
    Vivek F. Farias, Srikanth Jagabathula, Devavrat Shah B · 2012 · Conference on Information Sciences and Systems
62. Introducing the Expohedron for Efficient Pareto-optimal Fairness-Utility Amortizations in Repeated Rankings
    Till Kletti, Jean-Michel Renders, Patrick Loiseau · 2022 · ACM International Conference on Web Search and Data Mining (WSDM)
63. Supervised ordering by regression combined with Thurstone’s model
    Toshihiro Kamishima, Shotaro Akaho · 2006 · Artificial Intelligence Review
64. Randomized Kaczmarz for rank aggregation from pairwise comparisons
    Vivek S. Borkar, Nikhil Karamchandani, Sharad Mirani · 2016 · IEEE Information Theory Workshop (ITW)
65. Chapter 18 Efficiency of sports and lottery betting markets
    Donald B. Hausch, William T. Ziemba · 1995 · Handbooks in operations research and management science
66. Content-Aware Listwise Collaborative Filtering
    Rabeh Ravanifard, Abdolreza Mirzaei, Wray Buntine, Mehran Safayani · 2021 · Neurocomputing
67. Listwise Approach for Rank Aggregation in Crowdsourcing
    Shuzi Niu, Yanyan Lan, Jiafeng Guo, Xueqi Cheng, Lei Yu, Guoping Long · 2015 · ACM International Conference on Web Search and Data Mining (WSDM)
68. Top-$K$ Rank Aggregation From $M$-Wise Comparisons
    Minje Jang, Sunghyun Kim, Changho Suh · 2018 · IEEE Journal of Selected Topics in Signal Processing
69. Climate variability indices for ecological and crop models in R: the climatrends package
    Kauê de Sousa, Jacob van Etten, Magne Neby, Svein Øivind Sølberg · 2023 · The Journal of Open Source Software
70. Revisiting the Bradley-Terry model and its application to information retrieval
    Jong June Jeon, Yong-Dai Kim · 2013 · Journal of the Korean Data and Information Science Society
71. Parameter identification in Markov chain choice models
    Arushi Gupta, Daniel Hsu · 2019 · Theoretical Computer Science
72. Multinomial Logit Bandit with Linear Utility Functions
    Mingdong Ou, Nan Li, Shenghuo Zhu B, Rong Jin · 2018 · International Joint Conference on Artificial Intelligence
73. Parametric Tests for Agreement Amongst Groups of Judges
    Anthony N. Pettitt · 1982 · Biometrika
74. Ranking Aggregation Based on Belief Function Theory
    Andrea Argentini · 2012 · Unitn-eprints PhD (University of Trento)
75. Gosset: An R Package for Analysis and Synthesis of Ranking Data in Agricultural Experimentation
    Kauê de Sousa, David Brown, Jonathan Steinke, Jacob van Etten · 2022 · SoftwareX
76. A General Pairwise Comparison Model for Extremely Sparse Networks
    Ruijian Han, Yiming Xu, Kani Chen · 2022 · Journal of the American Statistical Association
77. Strategy-Proofness in the Stable Matching Problem with Couples
    Andrew Perrault, Joanna Drummond, Fahiem Bacchus A · 2016 · Adaptive Agents and Multi-Agents Systems
78. Incentive-Compatible Assortment Optimization for Sponsored Products
    Santiago Balseiro, Antoine Désir · 2022 · Management Science
79. Luck, skill, and depth of competition in games and social hierarchies
    Maximilian Jerdee, Mark E. J. Newman · 2024 · Science Advances
80. Learning to rank using query-level regression
    Jiajin Wu, Zhihao Yang, Yuan Lin, Hongfei Lin, Zheng Ye, Kan Xu · 2011 · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)
81. A variational EM method for mixed membership models with multivariate rank data: An analysis of public policy preferences
    Y. Samuel Wang, Ross L. Matsueda, Elena A. Erosheva · 2017 · The Annals of Applied Statistics
82. Monotonicity as a tool for differentiating between truth and optimality in the aggregation of rankings
    Raúl Pérez‐Fernández, Pedro Alonso, Irene Dı́az, Susana Montes A, Bernard De Baets C · 2017 · Journal of Mathematical Psychology
83. Automatic temporal ranking of children’s engagement levels using multi-modal cues
    Jaebok Kim, Khiet P. Truong, Vanessa Evers · 2017 · Computer Speech & Language
84. Hybrid Ranking and Regression for Algorithm Selection
    Jonas Hanselle, Alexander Tornede, Marcel Wever, Eyke Hüllermeier · 2020 · Lecture notes in computer science
85. Gradient search in the space of permutations
    Valentino Santucci, Josu Ceberio, Marco Baioletti · 2020 · Genetic and Evolutionary Computation Conference Companion (GECCO Companion)
86. Learning Interpretable Feature Context Effects in Discrete Choice
    Kiran Tomlinson, Austin R. Benson · 2021 · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
87. Partition–Mallows Model and Its Inference for Rank Aggregation
    Wanchuang Zhu, Yingkai Jiang, Jun S. Liu, Ke Deng · 2021 · Journal of the American Statistical Association
88. Probabilistic Permutation Graph Search: Black-Box Optimization for Fairness in Ranking
    Ali Vardasbi, F. Sarvi, M. de Rijke · 2022 · Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
89. ListMAP: Listwise learning to rank as maximum a posteriori estimation
    Sanaz Keshvari, Faezeh Ensan, Hadi Sadoghi Yazdi · 2022 · Information Processing & Management
90. A novel method for estimating the common signals for consensus across multiple ranked lists
    Vendula Švendová, Michael G. Schimek · 2017 · Computational Statistics & Data Analysis
91. Learning to rank using multiple loss functions
    Yuan Lin, Jiajin Wu, Bo Xu, Kan Xu, Hongfei Lin · 2017 · International Journal of Machine Learning and Cybernetics
92. The 2016 Wines of Portugal Challenge: general implications of more than 8400 wine-score observations
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93. PLMIX: anRpackage for modelling and clustering partially ranked data
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94. Ranking Distillation for Open-Ended Video Question Answering with Insufficient Labels
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95. AI Alignment at Your Discretion
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96. Probabilistic n-Choose-k Models for Classification and Ranking
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97. Model-based Gradient Search for Permutation Problems
    Josu Ceberio, Valentino Santucci · 2023 · ACM Transactions on Evolutionary Learning and Optimization
98. Uncovering the riffled independence structure of ranked data
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99. A markov chain approximation to choice modeling
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100. Testing a Mixture of Rank Preference Models on Judges' Scores in Paris and Princeton
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