The Analysis of Permutations

Cited by (732)

Items 501–600 of 732

1. Bayesian inference from rank data
   Øystein Sørensen, Valeria Vitelli, Arnoldo Frigessi, Elja Arjas · 2014 · arXiv (Cornell University)
2. Learning RUMs: Reducing Mixture to Single Component via PCA
   Devavrat Shah B, Dogyoon Song · 2018 · arXiv (Cornell University)
3. Fast and Robust Rank Aggregation against Model Misspecification
   Yuangang Pan, Weijie Chen, Niu, Gang, Ivor Wai-Hung Tsang, Masashi Sugiyama · 2019 · arXiv (Cornell University)
4. Plackett-Luce regression: A new Bayesian model for polychotomous data
   Cédric Archambeau, Caron, Francois · 2012 · arXiv (Cornell University)
5. A review on ranking problems in statistical learning
   Tino Werner · 2019 · arXiv (Cornell University)
6. A Multiclass Classification Approach to Label Ranking
   Stéphan Clémençon, Robin Vogel · 2020 · arXiv (Cornell University)
7. Two-Sample Testing on Ranked Preference Data and the Role of Modeling Assumptions
   Charvi Rastogi, Sivaraman Balakrishnan, Nihar B. Shah, Aarti Singh · 2020 · arXiv (Cornell University)
8. PiRank: Learning To Rank via Differentiable Sorting.
   Robin M. E. Swezey, Aditya Grover, Bruno Charron, Stefano Ermon · 2020 · arXiv (Cornell University)
9. Inferring a consensus problem list using penalized multistage models for ordered data
   Philip S. Boonstra, John C. Krauss · 2020 · The Annals of Applied Statistics
10. Label Enhancement via Joint Implicit Representation Clustering
    Yunan Lu, Weiwei Li, Xiuyi Jia · 2023 · International Joint Conference on Artificial Intelligence
11. Quasi-polynomial time approximation schemes for assortment optimization under Mallows-based rankings
    Alon Rieger, Danny Segev · 2023 · Mathematical Programming
12. Identifying Improvements in Treating Extremity Musculoskeletal Injuries During Prolonged Care
    William Brett Johnson, Antuione D Perry, Garrett Flores, Sarah N. Pierrie, Joseph F. Alderete, Paul Denney Allen, Jonathan B Wilson, David B. King, Walter Lee Childers · 2024 · Military Medicine
13. Modeling Preferences: A Bayesian Mixture of Finite Mixtures for Rankings and Ratings
    Michael Pearce, Elena A. Erosheva · 2024 · Journal of the American Statistical Association
14. Adaptive Preferences: Pivoting Through User Complaints
    Mingyue Zhang, Jialong Li, Nianyu Li, Eunsuk Kang, Kenji Tei · 2025 · ACM Transactions on Autonomous and Adaptive Systems
15. Perceptions of the Fairness Impacts of Multiplicity in Machine Learning
    Meyer, Anna P., Kim, Yea-Seul, D'Antoni, Loris, Albarghouthi, Aws · 2025 · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems
16. Amino acid exchangeability and surface accessibility underpin the effects of single substitutions
    Berk A. Alpay, Piyush Nanda, Emma Nagy, Michael M. Desai · 2026 · Genetics
17. Item Response Models for Rating Relational Data
    Chih-Han Leng, Ulf Böckenholt, Hsuan‐Wei Lee, Grace Yao · 2025 · Psychometrika
18. Learning to Rank with Variable Result Presentation Lengths
    Norman Knyazev, Harrie Oosterhuis · 2025 · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)
19. Wrong Answers Can Also Be Useful: P lausible QA - A Large-Scale QA Dataset with Answer Plausibility Scores
    Jamshid Mozafari, Abdelrahman Abdallah, Bhawna Piryani, Adam Jatowt · 2025 · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)
20. How to Train Algorithm Selection Models: Insights from Black-box Continuous Optimization
    Xiao He, Haopu Shang, Chao Qian · 2025 · Genetic and Evolutionary Computation Conference Companion (GECCO Companion)
21. Shaping Sustainable Tourist Experience: Simulating the Impact of Recommendation Salience
    Pavel Merinov, Francesco Ricci⋆ · 2025 · ACM Transactions on Recommender Systems
22. Sequential Rank and Preference Learning with the Bayesian Mallows Model
    Øystein Sørensen, Anja Stein, Waldir Leoncio Netto, David S. Leslie · 2025 · Bayesian Analysis
23. Is more always better? Measuring the quality of ranking data through information entropy
    Yishan Liu, Yu Xiao, Xin Long, Jun Ji Wu · 2025 · Journal of Data and Information Science
24. Rank aggregation to predict the fundamental frequency of historic masonry towers
    Alessio Crocetti, Gaetano Miraglia, Rosario Ceravolo · 2026 · Bulletin of Earthquake Engineering
25. “We Care More About What It Does”: Priorities and Responsibilities in VR for Children and Families
    Qiao Jin, Xiaoran Yang, Svetlana Yarosh · 2026 · IEEE Transactions on Visualization and Computer Graphics
26. How good is Tiger Woods
    Rose Baker, Ian G. McHale · 2012 · University of Salford Institutional Repository (University of Salford)
27. Asymptotic efficiency of majority rule relative to rank-sum method for selecting the best population
    Samuel Shangwu Wu, David H. Annis · 2006 · Journal of Statistical Planning and Inference
28. Estimating Independent Locally Shifted Random Utility Models for Ranking Data
    Kar Yin Lam, Alex Koning, Philip Hans Franses · 2011 · Multivariate Behavioral Research
29. The Listwise Approach
    Tie-Yan Liu · 2011 · Springer eBooks
30. A Probabilistic Rating System for Team Competitions with Individual Contributions
    Sergey Nikolenko · 2015 · Communications in computer and information science
31. Modelling Preference Data with the Wallenius Distribution
    Clara Grazian, Fabrizio Leisen, Brunero Liseo · 2018 · Journal of the Royal Statistical Society Series A (Statistics in Society)
32. Group Formation Based on Crowdsourced Top-k Recommendation
    Yunpeng Gao, Wei Cai, Kuiyang Liang · 2017 · Lecture notes in computer science
33. Improving document ranking with genetic and optimization algorithms
    Lawrence Master · 2017 · International Journal of Numerical Modelling Electronic Networks Devices and Fields
34. Dynamic Pricing and Demand Shaping: Theory and Applications in Online Assortments, Ride Sharing and Smart Grids
    Shuangyu Wang · 2019
35. Efficient Parameter Estimation of Truncated Boolean Product Distributions
    Dimitris A. Fotakis, Alkis Kalavasis, Christos Tzamos · 2022 · Algorithmica
36. On testing transitivity in online preference learning
    Björn Haddenhorst, Viktor Bengs, Eyke Hüllermeier · 2021 · Machine Learning
37. Generalized linear mixed model with bayesian rank likelihood
    Lyubov Doroshenko, Brunero Liseo · 2022 · Statistical Methods & Applications
38. Analysis of competitive surfing tournaments with generalized Bradley-Terry likelihoods
    Timothy C. Driver, Robin K. S. Hankin · 2023 · Journal of Sports Analytics
39. Comparing machine learning algorithms by union-free generic depth
    Hannah Blocher, Georg Schollmeyer, Malte Nalenz, Christoph Jansen · 2024 · International Journal of Approximate Reasoning
40. Athlete rating in multicompetitor games with scored outcomes via monotone transformations
    Jonathan Che, Mark E. Glickman · 2024 · The Annals of Applied Statistics
41. A Combinatorial Optimization Framework for Probability-Based Algorithms by Means of Generative Models
    Mikel Malagón, Ekhiñe Irurozki, Josu Ceberio · 2024 · ACM Transactions on Evolutionary Learning and Optimization
42. Generalized Plackett-Luce Likelihoods
    Robin K. S. Hankin · 2024 · Journal of Statistical Software
43. Optimizing Learning-to-Rank Models for Ex-Post Fair Relevance
    Sruthi Gorantla, Eshaan Bhansali, Amit Deshpande, Anand Louis · 2024 · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)
44. LT2R: Learning to Online Learning to Rank for Web Search
    Xiaokai Chu, Changying Hao, Shuaiqiang Wang, Dawei Yin, Jiashu Zhao, Lixin Zou, Chenliang Li · 2024 · IEEE International Conference on Data Engineering (ICDE)
45. Statistical models of ballot truncation in ranked choice elections
    Christina Hoffman, Jakini Auset Kauba, Julie C. Reidy, Thomas Weighill · 2024 · Communications in Statistics - Simulation and Computation
46. Offline Evaluation of Set-Based Text-to-Image Generation
    Negar Arabzadeh, Fernando Díaz, Junfeng He · 2024 · Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region (SIGIR-AP)
47. Costly matching problem and revenue-maximizing strategy in the platform economy
    Dipankar Das, Dheeraj Chandra · 2024 · Journal of Revenue and Pricing Management
48. Simulating the Impact of Recommendation Salience on Tourists Experienced Utility
    Pavel Merinov, Francesco Ricci⋆ · 2025 · Communications in computer and information science
49. Efficient inference of rankings from multibody comparisons
    Jack Yeung, Daniel Kaiser, Filippo Radicchi · 2025 · Physical review. E
50. Spectral Survival Analysis
    Chengzhi Shi, Stratis Ioannidis · 2025 · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
51. Local people’s preferences for housing development-associated Biodiversity Net Gain in England
    Amber Butler, Ben Groom, Eleanor Jane Milner‐Gulland · 2025 · Land Use Policy
52. Preferences in college applications - a nonparametric Bayesian analysis of top-10 rankings
    Alnur Ali, Thomas Brendan Murphy, Marina Meilă, Harr Chen · 2010 · Research Repository UCD (University College Dublin)
53. A new statistic for testing uniformity of ranking data
    Serdar Korukoğlu, Kadir Ertaş · 1996 · Communications in Statistics - Simulation and Computation
54. Leading the field: Fortune favors the bold in Thurstonian choice models
    Steven N. Evans, Ronald L. Rivest, Philip B. Stark · 2018 · Bernoulli
55. Choice Functions Generated by Mallows and Plackett–Luce Relations
    Vidal Kamdem Tagne, Siméon Fotso, Louis Aimé Fono, Eyke Hüllermeier · 2019 · New Mathematics and Natural Computation
56. Fall if it Lifts your Teammate: A Novel Type of Candidate Manipulation
    Justin Kruger, Sebastian Schneckenburger · 2019 · Adaptive Agents and Multi-Agents Systems
57. Instance-Sensitive Algorithms for Pure Exploration in Multinomial Logit Bandit
    Nikolai Karpov, Qin Zhang · 2022 · Proceedings of the AAAI Conference on Artificial Intelligence
58. Elicitability of Instance and Object Ranking
    Tino Werner · 2022 · Decision Analysis
59. Collective Schedules: Axioms and Algorithms
    Martin Durand, Fanny Pascual · 2022 · Lecture notes in computer science
60. A note on the use of rank‐ordered logit models for ordered response categories
    Timothy R. Johnson · 2022 · British Journal of Mathematical and Statistical Psychology
61. Uncovering the Riffled Independence Structure of Rankings
    Jonathan Huang, Carlos Guestrin · 2010 · arXiv (Cornell University)
62. Capturing Variation and Uncertainty in Human Judgment
    Mao, Andrew, Hossein Azari Soufiani, Yiling Chen, David C. Parkes · 2013 · arXiv (Cornell University)
63. Scaling Learning to Rank to Big Data: Using MapReduce to Parallelise Learning to Rank
    Niek Tax · 2014
64. Accommodating Stochastic Departures from Percentile Invariance in Causal Models
    Kevin K. Dobbin, Thomas A. Louis · 2003 · Journal of the Royal Statistical Society Series B (Statistical Methodology)
65. Ranking and Context-awareness of Recommender Systems
    Yue Shi · 2013 · Research Repository (Delft University of Technology)
66. ConeRANK: Ranking as Learning Generalized Inequalities
    Truyen Tran, Duc-Son Pham · 2012 · arXiv (Cornell University)
67. PLMIX: An R package for modeling and clustering partially ranked data
    Cristina Mollica, Luca Tardella · 2016 · arXiv (Cornell University)
68. Crowdsourcing for Engineering Design: Objective Evaluations and Subjective Preferences
    Alexander Burnap · 2016 · Deep Blue (University of Michigan)
69. Group decision making and preference learning on social networks
    Amirali Salehi‐Abari · 2016 · TSpace (University of Toronto)
70. Algorithms and diagnostics for the analysis of preference rankings with the Extended Plackett-Luce model
    Cristina Mollica, Luca Tardella · 2018 · arXiv (Cornell University)
71. Exchange traded horserace betting fund with deterministic payoff – a mathematical analysis of a profitable deterministic horserace betting model
    Craig George Leslie Hopf · 2014 · Griffith Research Online
72. A Roadmap for Robust End-to-End Alignment
    Lê Nguyên Hoang · 2018 · arXiv (Cornell University)
73. Modelling Outcomes in Canadian Professional Football via Generalized Bradley-Terry Models
    Daniel Scott Fleischhaker · 2019
74. A Bayesian Choice Model for Eliminating Feedback Loops
    Gökhan Çapan, İlker Gündoğdu, Ali Caner Türkmen, Çağrı Sofuoğlu, Ali Taylan Cemgil · 2019 · arXiv (Cornell University)
75. Analysis of Regression Tree Fitting Algorithms in Learning to Rank
    Tian Xia, Shaodan Zhai, Shaojun Wang · 2019 · arXiv (Cornell University)
76. Plackett-Luce model for learning-to-rank task
    Xia, Tian, Shaodan Zhai, Shaojun Wang · 2019 · arXiv (Cornell University)
77. Interactive Text Ranking with Bayesian Optimisation: A Case Study on Community QA and Summarisation
    Edwin Simpson, Yang Gao, Iryna Gurevych · 2019 · arXiv (Cornell University)
78. Aggregating Incomplete and Noisy Rankings
    Dimitris A. Fotakis, Alkis Kalavasis, Konstantinos S. Stavropoulos · 2020 · arXiv (Cornell University)
79. Leveraging probability concepts for genotype by environment recommendation
    Kaio Olímpio das Graças Dias, Jhonathan Pedroso Rigal dos Santos, Matheus Dalsente Krause, Hans‐Peter Piepho, L. J. M. Guimarães, Maria Marta Pastina, Antônio Augusto Franco Garcia · 2021 · bioRxiv (Cold Spring Harbor Laboratory)
80. Adaptive Price Mechanism and a Sequential Reverse Negotiation Model in Social Commerce
    Dipankar Das · 2021 · SSRN Electronic Journal
81. Pricing a Finite Inventory of Substitutable Products with Show-All Constraint
    Vineet Goyal, Garud Iyengar, Rajan Udwani · 2022 · SSRN Electronic Journal
82. Covariate Assisted Entity Ranking with Sparse Intrinsic Scores
    Jianqing Fan A B, Jikai Hou, Mengxin Yu · 2024 · SSRN Electronic Journal
83. Prioritization of Perceptual Decisions
    Daniel R. Little, Ruby Steinberg, Asha Bartlett, Luke Russell, Peizhao Xie, Ami Eidels · 2025
84. Low-Variance Black-Box Gradient Estimates for the Plackett-Luce Distribution
    Artyom Gadetsky, Kirill Struminsky, Chris Robinson, Novi Quadrianto, Dmitry Vetrov · 2020 · Proceedings of the AAAI Conference on Artificial Intelligence
85. Informative bayesian modeling with applications to media data
    Pengyuan Wang · 2012 · ScholarlyCommons (University of Pennsylvania)
86. Gambling, Statistics in
    David Griffiths · 2014 · Wiley StatsRef: Statistics Reference Online
87. A Monte Carlo Study of the Optimal Rank Order Relationships with Criterion Scores (ORORCS)
    Hongyan Cui, Brooks E. Applegate, Warren E. Lacefield, David Hartmann · 2014
88. A mathematical analysis of an exchange-traded horse race betting fund with deterministic payoff betting strategy for institutional investment to challenge EMH
    Craig George Leslie Hopf, Gurudeo Anand Tularam · 2015 · Cogent Mathematics
89. Ranking Models, Mathematics of
    Douglas E. Critchlow · 2001 · Elsevier eBooks
90. Statistics in Gambling
    David Griffiths · 2005 · Encyclopedia of Statistical Sciences
91. Permutation Models for Collaborative Ranking
    Truyen Tran, Svetha Venkatesh · 2014 · arXiv (Cornell University)
92. CloudRank: A statistical modelling framework for characterizing user behaviour towards targeted cloud management
    Sakyajit Bhattacharya, Tridib Mukherjee, Koustuv Dasgupta · 2014 · IEEE/IFIP Network Operations and Management Symposium (NOMS)
93. Eliciting and Aggregating Truthful and Noisy Information
    Xi Alice Gao · 2014 · Digital Access to Scholarship at Harvard (DASH) (Harvard University)
94. Estimating Probability Distributions by Observing Betting Practices
    Caroline Lynch, Daniel Barry · 2007 · Communication in Statistics- Theory and Methods
95. Some methodological issues on multivariate modelling of rank data
    Angela D’Elia · 2010
96. Mechanism Design for Multi-dimensional Facility Location Problems: A Computational and Informational Perspective
    Xin Sui · 2015 · TSpace (University of Toronto)
97. Object ranking
    Roelof van Zwol, Srinivas Vadrevu · 2011 · ACM International Conference on Information and Knowledge Management (CIKM)
98. Contribution à l'apprentissage statistique à base de modèles génératifs pour données complexes.
    Julien Jacques · 2012 · HAL
99. Universality of Mallows' and degeneracy of Kendall's kernels for rankings.
    Horia Mania, Aaditya Ramdas, Martin J. Wainwright, Michael I. Jordan, Benjamin Recht · 2016 · arXiv (Cornell University)
100. Learning Rank Functionals: An Empirical Study
     Truyen Tran, Dinh Phung, Svetha Venkatesh · 2014 · arXiv (Cornell University)