Comparison-Based Learning: Algorithms and Applications
A special issue of Algorithms (ISSN 1999-4893).
Deadline for manuscript submissions: closed (31 January 2021) | Viewed by 397
Special Issue Editor
Special Issue Information
Dear Colleagues,
We invite you to submit your work to this Algorithms Special Issue on “Comparison-Based Learning”. Learning from comparisons between samples, or rankings thereof, finds applications in many domains, including econometrics, recommender systems, and sports. Collecting comparison or ranking labels has additional benefits in practice, as humans often find it easier to make relative rather than absolute judgements. From a theoretical standpoint, even though learning via comparison and/or ranking oracles often comes with statistical advantages, it also poses algorithmic challenges due to the combinatorial nature of comparisons and rankings.
We seek high-quality contributions in all theoretical and practical aspects of algorithms for learning from comparisons and rankings.
Dr. Stratis Ioannidis
Guest Editor
Manuscript Submission Information
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Keywords
- Plackett–Luce, Bradley–Terry, and Thurstone models
- Mallows model
- Non-parametric models
- Top-K ranking
- Efficient inference algorithms
- Active ranking
- Active learning from comparisons
- Spectral methods for ranking
- PAC learning from comparisons and rankings
- Classification via comparison queries
- Comparison-based search
- Learning to rank
- Rank aggregation
- Ranking and ordinal regression from comparisons
- Ranking model mixtures
- Minimum feedback arc set and applications to learning
- Noisy sorting
- Deep learning methods for learning to rank
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