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Learning-To-Rank

Learning to rank is the application of machine learning to build ranking models. Some common use cases for ranking models are information retrieval (e.g., web search) and news feeds application (think Twitter, Facebook, Instagram).

Papers

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Showing 261–270 of 753 papers

Title	Date	Tasks	Status	Hype
Unbiased Top-k Learning to Rank with Causal Likelihood Decomposition	Apr 2, 2022	Learning-To-RankPosition	CodeCode Available	0
Unimodal-Concentrated Loss: Fully Adaptive Label Distribution Learning for Ordinal Regression	Apr 1, 2022	Head Pose EstimationLearning-To-Rank	CodeCode Available	1
Doubly-Robust Estimation for Correcting Position-Bias in Click Feedback for Unbiased Learning to Rank	Mar 31, 2022	counterfactualGeneral Reinforcement Learning	CodeCode Available	0
Minimax Regret for Cascading Bandits	Mar 23, 2022	Learning-To-Rank	—Unverified	0
Personalized Execution Time Optimization for the Scheduled Jobs	Mar 11, 2022	Learning-To-RankRecommendation Systems	—Unverified	0
Evaluating Local Model-Agnostic Explanations of Learning to Rank Models with Decision Paths	Mar 4, 2022	Feature ImportanceLearning-To-Rank	—Unverified	0
Distilled Neural Networks for Efficient Learning to Rank	Feb 22, 2022	CPUInformation Retrieval	CodeCode Available	0
Learning to Rank from Relevance Judgments Distributions	Feb 13, 2022	Learning-To-Rank	CodeCode Available	0
Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference	Feb 12, 2022	Entity TypingLearning-To-Rank	CodeCode Available	1
A new perspective on classification: optimally allocating limited resources to uncertain tasks	Feb 9, 2022	Fraud DetectionLearning-To-Rank	—Unverified	0

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