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Comparison-Based Learning: Algorithms and Applications

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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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Algorithms - ISSN 1999-4893
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