The Analysis of Permutations

Cited by (732)

Items 101–200 of 732

1. Online rank elicitation for Plackett-Luce: a dueling bandits approach
   Baiÿzs Szörényi, Róbert Busa‐Fekete, Adil Paul, Eyke Hüllermeier · 2015 · Open access LMU (Ludwid Maxmilian's Universitat Munchen)
2. A generative model for rank data based on insertion sort algorithm
   Christophe Biernacki, Julien Jacques · 2012 · Computational Statistics & Data Analysis
3. Tractable Learning for structured probability spaces: A case study in learning preference distributions
   Arthur Choi, Guy Van den Broeck, Adnan Y. Darwiche · 2015 · Lirias
4. Policy-Gradient Training of Fair and Unbiased Ranking Functions
   Himank Yadav, Zhengxiao Du, Thorsten Joachims · 2021 · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)
5. Computing Parametric Ranking Models via Rank-Breaking
   Hossein Azari Soufiani, David C. Parkes, Lirong Xia · 2014 · Digital Access to Scholarship at Harvard (DASH) (Harvard University)
6. Consumer Preference Testing of Boiled Sweetpotato Using Crowdsourced Citizen Science in Ghana and Uganda
   Mukani Moyo, Reuben Ssali, S. Namanda, Mariam Nakitto, Eric Kuuna Dery, Daniel A. Akansake, Joseph Adjebeng‐Danquah, Jacob van Etten, Kauê de Sousa, Hannele Lindqvist‐Kreuze et al. · 2021 · Frontiers in Sustainable Food Systems
7. Learning Mixtures of Random Utility Models
   Zhibing Zhao, Tristan Villamil, Lirong Xia · 2018 · Proceedings of the AAAI Conference on Artificial Intelligence
8. New learning methods for supervised and unsupervised preference aggregation
   Maksims Volkovs, Richard S. Zemel · 2014
9. Assortment Optimization Under the Multi-Purchase Multinomial Logit Choice Model
   Yicheng Bai, Jacob Feldman, Danny Segev, Hüseyin Topaloğlu, Laura Wagner · 2023 · Operations Research
10. Preference-Based Rank Elicitation using Statistical Models: The Case of Mallows
    Róbert Busa‐Fekete, Eyke Hüllermeier, Balázs Szörényi, Xing, Eric P., Tony Jebara · 2014 · SZTE Publicatio Repozitórium (University of Szeged)
11. Capacity Constrained Assortment Optimization Under the Markov Chain Based Choice Model
    Antoine DDsir, Vineet Goyal, Danny Segev, Chun Ye · 2015 · SSRN Electronic Journal
12. Aligning protein generative models with experimental fitness via Direct Preference Optimization
    Talal Widatalla, Rafael Rafailov, Brian Hie · 2024 · bioRxiv (Cold Spring Harbor Laboratory)
13. Genomics-driven breeding for local adaptation of durum wheat is enhanced by farmers’ traditional knowledge
    Cherinet Alem, Bogale Nigir, Kauê de Sousa, Luca Gianfranceschi, Guido Roberto Gallo, Jesse Poland, Yosef Gebrehawaryat, Ermias Abate Desta, Carlo Fadda, Mario Enrico Pè et al. · 2023 · Proceedings of the National Academy of Sciences
14. Analysis of ranking data
    Philip L. H. Yu, Jiaqi Gu, Hang Xu · 2019 · Wiley Interdisciplinary Reviews Computational Statistics
15. A Taxonomy of Label Ranking Algorithms
    Yangming Zhou, Yangguang Liu, Jiangang Yang, Xiaoqi He, Liangliang Liu · 2014 · Journal of Computers
16. Diverse Randomized Agents Vote to Win
    Albert Xin Jiang, Leandro Soriano Marcolino, Ariel D. Procaccia, Tüomas Sandholm, Nisarg Shah, Milind Tambe · 2018 · Figshare
17. Model-based clustering for multivariate partial ranking data
    Julien Jacques, Christophe Biernacki A · 2014 · Journal of Statistical Planning and Inference
18. The Plackett-Luce ranking model on permutation-based optimization problems
    Josu Ceberio, Alexander Mendiburu, Jose Antonio Lozano · 2013 · IEEE Congress on Evolutionary Computation (CEC)
19. Supervised Ordering — An Empirical Survey
    Toshihiro Kamishima, Hideto Kazawa, Shotaro Akaho · 2006 · Proceedings
20. Toward Continual Learning for Conversational Agents
    Lee, Sungjin · 2017 · arXiv (Cornell University)
21. Sampling and Learning Mallows and Generalized Mallows Models Under the Cayley Distance
    Ekhiñe Irurozki, Borja Calvo, Jose Antonio Lozano · 2016 · Methodology And Computing In Applied Probability
22. Fair Learning-to-Rank from Implicit Feedback.
    Himank Yadav, Zhengxiao Du, Thorsten Joachims · 2019
23. Mixed-Effects Analyses of Rank-Ordered Data
    Ulf Böckenholt · 2001 · Psychometrika
24. A Near-Optimal Exploration-Exploitation Approach for Assortment Selection
    Shipra Agrawal, Vashist Avadhanula, Vineet Goyal, Assaf J. Zeevi · 2016 · ACM Conference on Economics and Computation (EC)
25. Predicting Humorousness and Metaphor Novelty with Gaussian Process Preference Learning
    Edwin Simpson, Erik-Lân Do Dinh, Tristan Miller, Iryna Gurevych · 2019 · Proceedings of the conference - Association for Computational Linguistics. Meeting
26. Preference-based Online Learning with Dueling Bandits: A Survey
    Viktor Bengs, Róbert Busa‐Fekete, Adil El Mesaoudi-Paul, Eyke Hüllermeier · 2018 · Open access LMU (Ludwid Maxmilian's Universitat Munchen)
27. On the likelihood of single-peaked preferences
    Marie-Louise Lackner, Martin Lackner · 2017 · Social Choice and Welfare
28. A new correlation coefficient for comparing and aggregating non-strict and incomplete rankings
    Yeawon Yoo, Adolfo R. Escobedo, J. Kyle Skolfield · 2020 · European Journal of Operational Research
29. A technical survey on statistical modelling and design methods for crowdsourcing quality control
    Jin, Yuan, Carman, Mark, Zhu, Ye, Xiang, Yong · 2020 · Artificial Intelligence
30. Exponential-family Random Graph Models for Rank-order Relational Data
    Pavel N. Krivitsky, Carter T. Butts · 2017 · Sociological Methodology
31. Public Perceptions of Pricing Existing Roads and Other Transportation Policies: The Texas Perspective
    Kara Maria Kockelman, Kaethe V. Podgorski, Michelle Bina, Shashank Gadda · 2012 · Journal of the Transportation Research Forum
32. The unavailable candidate model
    Tyler Lu, Craig E. Boutilier · 2010 · ACM Conference on Electronic Commerce (EC)
33. A new decision making model based on Rank Centrality for GDM with fuzzy preference relations
    Anis Yazidi, Magdalena Ivanovska, Fabio Massimo Zennaro, Pedro G. Lind, Enrique Herrera‐Viedma · 2021 · European Journal of Operational Research
34. Leveraging probability concepts for cultivar recommendation in multi-environment trials
    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 · 2022 · Theoretical and Applied Genetics
35. Designing social choice mechanisms using machine learning
    Lirong Xia · 2013
36. A survey on learning to rank
    Chuan He, Cong Wang, Yixin Zhong, Ruifan Li · 2008 · International Conference on Machine Learning and Cybernetics
37. Learning Plackett-Luce Mixtures from Partial Preferences
    Ao Liu, Zhibing Zhao, Chao Liao, Pinyan Lu, Lirong Xia · 2019 · Proceedings of the AAAI Conference on Artificial Intelligence
38. The tricot approach: an agile framework for decentralized on-farm testing supported by citizen science. A retrospective
    Kauê de Sousa, Jacob van Etten, Rhys Manners, Erna Abidin, Rekiya O. Abdulmalik, Abolore Bello, Kwabena Acheremu, Stephen Angudubo, Amílcar Aguilar, Elizabeth Arnaud et al. · 2024 · Agronomy for Sustainable Development
39. Hierarchical Models for Permutations
    Todd Graves, C. Shane Reese, Mark Fitzgerald · 2003 · Journal of the American Statistical Association
40. Some remarks on nevzorov's record model
    Dietmar Pfeifer · 1991 · Advances in Applied Probability
41. Hybrid-MST: A Hybrid Active Sampling Strategy for Pairwise Preference Aggregation
    Jing Li, Rafał Mantiuk, Junle Wang, Suiyi Ling, Patrick Le Callet · 2018 · arXiv (Cornell University)
42. Direct optimization of ranking measures for learning to rank models
    Ming Tan, Tian Xia, Lily Guo, Shaojun Wang · 2013 · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
43. Neural Utterance Ranking Model for Conversational Dialogue Systems
    Michimasa Inaba, Keníchi Takahashi · 2016 · Annual Meeting of the Special Interest Group on Discourse and Dialogue
44. Dyad ranking using Plackett–Luce models based on joint feature representations
    Dirk Schäfer, Eyke Hüllermeier · 2018 · Machine Learning
45. A stochastic rank ordered logit model for rating multi-competitor games and sports
    Mark E. Glickman, Jonathan Hennessy · 2015 · Journal of Quantitative Analysis in Sports
46. Learning Mixtures of Plackett-Luce Models
    Zhibing Zhao, Peter Piech, Lirong Xia · 2016 · arXiv (Cornell University)
47. Learning to rank by aggregating expert preferences
    Maksims Volkovs, Hugo Larochelle, Richard S. Zemel · 2012 · ACM International Conference on Information and Knowledge Management (CIKM)
48. Supervised clustering of label ranking data using label preference information
    Mihajlo Grbovic, Nemanja Djuric, Shengbo Guo, Slobodan Vučetić · 2013 · Machine Learning
49. Posterior Probabilities for a Consensus Ordering
    Michael A. Fligner, Joseph S. Verducci · 1990 · Psychometrika
50. Optimizing Positional Scoring Rules for Rank Aggregation
    Ioannis Caragiannis, Xenophon Chatzigeorgiou, George A. Krimpas, Alexandros A. Voudouris · 2017 · Artificial Intelligence
51. Asymptotically Optimal Competitive Ratio for Online Allocation of Reusable Resources
    Vineet Goyal, Garud Iyengar, Rajan Udwani · 2025 · Operations Research
52. Inference in Opinion Dynamics Under Social Pressure
    Ali Jadbabaie, Anuran Makur, Elchanan Mossel, Rabih Salhab · 2022 · IEEE Transactions on Automatic Control
53. Bayesian Thurstonian models for ranking data using JAGS
    Timothy R. Johnson, Kristine M. Kuhn · 2013 · Behavior Research Methods
54. Evaluating Wine-Tasting Results and Randomness with a Mixture of Rank Preference Models
    Jeffrey C. Bodington · 2015 · Journal of Wine Economics
55. Representative & Informative Query Selection for Learning to Rank using Submodular Functions
    Rishabh Mehrotra, Emine Yılmaz · 2015 · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)
56. Accelerated Spectral Ranking
    Arpit Agarwal, Prathamesh Patil, Shivani Agarwal · 2018 · International Conference on Machine Learning
57. Combining phenotypic data from ordinal rating scales in multiple plant experiments
    Ivan Šimko, Hans‐Peter Piepho · 2011 · Trends in Plant Science
58. Analysis of the Formation of the Structure of Social Networks by Using Latent Space Models for Ranked Dynamic Networks
    Daniel K. Sewell, Yuguo Chen · 2015 · Journal of the Royal Statistical Society Series C (Applied Statistics)
59. 8. Permutation Models for Relational Data
    Carter T. Butts · 2007 · Sociological Methodology
60. Multilevel and Latent Variable Modeling with Composite Links and Exploded Likelihoods
    Sophia Rabe‐Hesketh, Anders Skrondal · 2007 · Psychometrika
61. Debiasing Crowdsourced Batches
    Honglei Zhuang, Aditya Parameswaran, Dan Roth, Jiawei Han · 2015 · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
62. An Exponential Model for Infinite Rankings
    Marina Meilă, Le Bao · 2010
63. Estimating Gradients for Discrete Random Variables by Sampling without Replacement
    Wouter Kool, Herke van Hoof, Max Welling · 2020 · arXiv (Cornell University)
64. Reaching the End of Unbiasedness
    Harrie Oosterhuis · 2022 · ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR)
65. Learning to rank from Bayesian decision inference
    Jen-Wei Kuo, Pu‐Jen Cheng, Hsin‐Min Wang · 2009 · ACM International Conference on Information and Knowledge Management (CIKM)
66. Thompson Sampling for Multinomial Logit Contextual Bandits
    Min-hwan Oh, Garud Iyengar · 2019 · Seoul National University Open Repository (Seoul National University)
67. Ordinal Data: An Alternative Distribution
    Robert S. Schulman · 1979 · Psychometrika
68. Assessing Goodness of Fit of Ranking Models to Data
    Ayala Cohen, Colin L. Mallows · 1983 · Journal of the Royal Statistical Society Series D (The Statistician)
69. Learning from Comparisons and Choices
    Sahand N. Negahban, Sewoong Oh, Kiran Koshy Thekumparampil, Jiaming Xu · 2017 · arXiv (Cornell University)
70. Learning Neural Causal Models with Active Interventions
    Nino Scherrer, Olexa Bilaniuk, Yashas Annadani, Anirudh Goyal, Patrick Schwab, Bernhard Schölkopf, Michael C. Mozer, Yoshua Bengio, Stefan Bauer, Nan Rosemary Ke · 2021 · arXiv (Cornell University)
71. Learning and Decision-Making from Rank Data
    Lirong Xia · 2019 · Synthesis lectures on artificial intelligence and machine learning
72. Household-specific targeting of agricultural advice via mobile phones: Feasibility of a minimum data approach for smallholder context
    Jonathan Steinke, Jerusha Onyango Achieng, James Hammond, Selamawit Sileshi Kebede, Dejene K. Mengistu, Majuto Gaspar Mgimiloko, Jemal Nurhisen Mohammed, Joseph Musyoka, Stefan Sieber, Jeske van de Gevel et al. · 2019 · Computers and Electronics in Agriculture
73. Optimal full ranking from pairwise comparisons
    Pinhan Chen, Chao Gao, Anderson Y. Zhang · 2022 · The Annals of Statistics
74. When Is Assortment Optimization Optimal?
    Will Ma · 2022 · Management Science
75. Neural Re-ranking in Multi-stage Recommender Systems: A Review
    Weiwen Liu, Yunjia Xi, Jiarui Qin, Fei Sun, Bo Chen, Weinan Zhang, Rui Zhang, Ruiming Tang · 2022 · International Joint Conference on Artificial Intelligence
76. Set-to-Sequence Ranking-Based Concept-Aware Learning Path Recommendation
    Xianyu Chen, Jian Shen, Wei Xia, Jiarui Jin, Yakun Song, Weinan Zhang, Weiwen Liu, Menghui Zhu, Ruiming Tang, Kai Dong et al. · 2023 · Proceedings of the AAAI Conference on Artificial Intelligence
77. Advances in Machine Learning for the Behavioral Sciences
    Tomáš Kliegr, Štěpán Bahník, Johannes Fürnkranz · 2019 · American Behavioral Scientist
78. Epitope profiling via mixture modeling of ranked data
    Cristina Mollica, Luca Tardella · 2014 · Statistics in Medicine
79. BayesRank
    Andrew E. Waters, David Tinapple, Richard G. Baraniuk · 2015 · ACM Conference on Learning @ Scale (L@S)
80. Integrating Knowledge Distillation With Learning to Rank for Few-Shot Scene Classification
    Yishu Liu, Liqiang Zhang, Zhengzhuo Han, Conghui Chen · 2021 · IEEE Transactions on Geoscience and Remote Sensing
81. Probability models on horse-race outcomes
    Mukhtar M. Ali · 1998 · Journal of Applied Statistics
82. Uncertainty quantification in the Bradley–Terry–Luce model
    Gao, Chao, Shen, Yandi, Zhang, Anderson Y · 2023 · Information and Inference: A Journal of the IMA
83. Genomic evolution shapes prostate cancer disease type
    Dan J. Woodcock, Atef Sahli, Ruxandra Teslo, Vinayak Bhandari, Andreas J Gruber, Aleksandra Ziubroniewicz, Gunes Gundem, Yaobo Xu, Adam P. Butler, Ezequiel Anokian et al. · 2024 · Cell Genomics
84. Dissimilarity for partially ranked data and its application to cover-abundance data
    Michael B. Dale · 1989 · Vegetatio
85. Mallows and generalized Mallows model for matchings
    Ekhiñe Irurozki, Borja Calvo, Jose Antonio Lozano · 2019 · Bernoulli
86. Analysis of ranked preference data using latent class models
    Évelyne Vigneau, Ph. Courcoux, Michel Séménou · 1999 · Food Quality and Preference
87. A Comparison of Truncated and Time-Weighted Plackett–Luce Models for Probabilistic Forecasting of Formula One Results
    Daniel A. Henderson, Liam J. Kirrane · 2017 · Bayesian Analysis
88. Display Optimization for Vertically Differentiated Locations Under Multinomial Logit Choice Preferences
    Ali Aouad, Danny Segev · 2015 · SSRN Electronic Journal
89. Top-$K$ Ranking from Pairwise Comparisons: When Spectral Ranking is Optimal
    Minje Jang, Sunghyun Kim, Changho Suh, Sewoong Oh · 2016 · arXiv (Cornell University)
90. ClimMob: Software to support experimental citizen science in agriculture
    Carlos F. Quirós, Kauê de Sousa, Jonathan Steinke, B. Madriz, Marie‐Angélique Laporte, Elizabeth Arnaud, Rhys Manners, Berta Ortiz-Crespo, Anna Müller, Jacob van Etten · 2023 · Computers and Electronics in Agriculture
91. Mixed Membership Matrix Factorization
    Lester W. Mackey, David Weiss, Michael I. Jordan · 2014 · Chapman & Hall/CRC eBooks
92. Efficiently Learning Mixtures of Mallows Models
    Allen Liu, Ankur Moitra · 2018 · Annual Symposium on Foundations of Computer Science
93. Learning to rank with relational graph and pointwise constraint for cross-modal retrieval
    Qingzhen Xu, Miao Li, Mengjing Yu · 2018 · Soft Computing
94. Ancestral Gumbel-Top-k Sampling for Sampling Without Replacement
    Wouter Kool, Herke van Hoof, Max Welling · 2020 · UvA-DARE (University of Amsterdam)
95. Isotonic regression with unknown permutations: Statistics, computation and adaptation
    Ashwin Pananjady, Richard J. Samworth · 2022 · The Annals of Statistics
96. A Note on Permutation Probabilities
    B. R. Dansie · 1983 · Journal of the Royal Statistical Society Series B (Statistical Methodology)
97. Recent Advances in Multiarmed Bandits for Sequential Decision Making
    Shipra Agrawal · 2019
98. Unfolding and group consensus ranking for individual preferences
    Robert J. Henery · 1992 · Journal of Classification
99. Ranking a Set of Objects: A Graph Based Least-Square Approach
    Evgenia Christoforou, Alessandro Nordio, Alberto Tarable, Emilio Leonardi · 2021 · IEEE Transactions on Network Science and Engineering
100. Drivers of consumer acceptability of cassava gari‐eba food products across cultural and environmental settings using the triadic comparison of technologies approach (tricot)
     Olamide Olaosebikan, Abolore Bello, Kauê de Sousa, Robert Ndjouenkeu, Michael Adesokan, Emmanuel Oladeji Alamu, Afolabi Agbona, Jacob van Etten, Franklin Ngoualem Kégah, Dominique Dufour et al. · 2023 · Journal of the Science of Food and Agriculture