Research Explorer
Papers
Conferences
Authors
Topics
Keywords
Trends
Achievements
Explore
← Learning Types
Machine Learning
›
Learning Types
›
Ranking
95 directly classified papers
Papers per year
2007: 2
2008: 1
2009: 5
2010: 1
2011: 2
2012: 7
2013: 3
2014: 6
2015: 2
2016: 1
2017: 6
2018: 6
2019: 14
2020: 5
2021: 6
2022: 6
2023: 8
2024: 8
2025: 6
Papers
Convex Calibrated Surrogates for Low-Rank Loss Matrices with Applications to Subset Ranking Losses
NIPS 2013
General Oracle Inequalities for Gibbs Posterior with Application to Ranking
COLT 2013
Probabilistic n-Choose-k Models for Classification and Ranking
NIPS 2012
Iterative ranking from pair-wise comparisons
NIPS 2012
Label Ranking with Partial Abstention based on Thresholded Probabilistic Models
NIPS 2012
Statistical Consistency of Ranking Methods in A Rank-Differentiable Probability Space
NIPS 2012
On the (Non-)existence of Convex, Calibrated Surrogate Losses for Ranking
NIPS 2012
On Multilabel Classification and Ranking with Partial Feedback
NIPS 2012
Efficient Sampling for Bipartite Matching Problems
NIPS 2012
Active Ranking using Pairwise Comparisons
NIPS 2011
Active Learning Ranking from Pairwise Preferences with Almost Optimal Query Complexity
NIPS 2011
Two-Layer Generalization Analysis for Ranking Using Rademacher Average
NIPS 2010
Statistical Consistency of Top-k Ranking
NIPS 2009
Polynomial Semantic Indexing
NIPS 2009
Learning to Rank by Optimizing NDCG Measure
NIPS 2009
AUC optimization and the two-sample problem
NIPS 2009
Ranking Measures and Loss Functions in Learning to Rank
NIPS 2009
Structured ranking learning using cumulative distribution networks
NIPS 2008
On Ranking in Survival Analysis: Bounds on the Concordance Index
NIPS 2007
McRank: Learning to Rank Using Multiple Classification and Gradient Boosting
NIPS 2007
<
1
2
3
4
>