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← Core Methods
Machine Learning
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Core Methods
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Ranking
216 directly classified papers
Papers per year
2003: 1
2006: 1
2007: 4
2008: 5
2009: 7
2010: 3
2011: 4
2012: 9
2013: 9
2014: 13
2015: 2
2016: 5
2017: 10
2018: 15
2019: 20
2020: 11
2021: 18
2022: 20
2023: 16
2024: 20
2025: 21
2026: 2
Papers
Riffled Independence for Ranked Data
NIPS 2009
Ranking Measures and Loss Functions in Learning to Rank
NIPS 2009
Exponential Family Graph Matching and Ranking
NIPS 2009
Statistical Consistency of Top-k Ranking
NIPS 2009
Learning to Rank by Optimizing NDCG Measure
NIPS 2009
Structured ranking learning using cumulative distribution networks
NIPS 2008
Reconciling Real Scores with Binary Comparisons: A New Logistic Based Model for Ranking
NIPS 2008
Empirical performance maximization for linear rank statistics
NIPS 2008
Overlaying classifiers: a practical approach for optimal ranking
NIPS 2008
Inferring rankings under constrained sensing
NIPS 2008
Non-parametric Modeling of Partially Ranked Data
NIPS 2007
On Ranking in Survival Analysis: Bounds on the Concordance Index
NIPS 2007
A General Boosting Method and its Application to Learning Ranking Functions for Web Search
NIPS 2007
McRank: Learning to Rank Using Multiple Classification and Gradient Boosting
NIPS 2007
Learning to Rank with Nonsmooth Cost Functions
NIPS 2006
A Family of Additive Online Algorithms for Category Ranking
JMLR 2003
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