1. Editorial: Preference learning and ranking

   • Eyke Hüllermeier
   • Johannes Fürnkranz

   Editorial 31 August 2013 Pages: 185 - 189

   

2. Supervised clustering of label ranking data using label preference information

   • Mihajlo Grbovic
   • Nemanja Djuric
   • Slobodan Vucetic

   OriginalPaper 07 June 2013 Pages: 191 - 225

   

3. Calibration and regret bounds for order-preserving surrogate losses in learning to rank

   • Clément Calauzènes
   • Nicolas Usunier
   • Patrick Gallinari

   OriginalPaper 14 August 2013 Pages: 227 - 260

   

4. Tune and mix: learning to rank using ensembles of calibrated multi-class classifiers

   • Róbert Busa-Fekete
   • Balázs Kégl
   • György Szarvas

   OriginalPaper 14 June 2013 Pages: 261 - 292

   

5. BoostingTree: parallel selection of weak learners in boosting, with application to ranking

   • Levente Kocsis
   • András György
   • Andrea N. Bán

   OriginalPaper 07 June 2013 Pages: 293 - 320

   

6. Efficient regularized least-squares algorithms for conditional ranking on relational data

   • Tapio Pahikkala
   • Antti Airola
   • Willem Waegeman

   OriginalPaper 07 June 2013 Pages: 321 - 356

   

7. Sequential event prediction

   • Benjamin Letham
   • Cynthia Rudin
   • David Madigan

   OriginalPaper 08 June 2013 Pages: 357 - 380

   

8. Robust ordinal regression in preference learning and ranking

   • Salvatore Corrente
   • Salvatore Greco
   • Roman Słowiński

   OriginalPaper Open access 27 July 2013 Pages: 381 - 422