@InProceedings{busa-fekete11a,
  title = {Ranking by calibrated AdaBoost},
  author = {R. Busa-Fekete and B. KÃ©gl and T. ÃltetÅ  and  G. Szarvas},
  pages = {37--48},
  abstract = {This paper describes the ideas and methodologies that we used in the Yahoo learning-to-rank challenge^{1}. Our technique is essentially pointwise with a listwise touch at the last combination step. The main ingredients of our approach are 1) preprocessing (querywise normalization) 2) multi-class AdaBoost.MH 3) regression calibration, and 4) an exponentially weighted forecaster for model combination. In post-challenge analysis we found that preprocessing and training AdaBoost with a wide variety of hyperparameters improved individual models significantly, the final listwise ensemble step was crucial, whereas calibration helped only in creating diversity.},
  pdf = {http://jmlr.org/proceedings/papers/v14/busa-fekete11a/busa-fekete11a.pdf},
}

@InProceedings{busa-fekete11a,
  title = {Ranking by calibrated AdaBoost},
  author = {R. Busa-Fekete and B. Kégl and T. Éltető and G. Szarvas},
  pages = {37--48},
  abstract = {This paper describes the ideas and methodologies that we used in the Yahoo learning-to-rank challenge^{1}. Our technique is essentially pointwise with a listwise touch at the last combination step. The main ingredients of our approach are 1) preprocessing (querywise normalization) 2) multi-class AdaBoost.MH 3) regression calibration, and 4) an exponentially weighted forecaster for model combination. In post-challenge analysis we found that preprocessing and training AdaBoost with a wide variety of hyperparameters improved individual models significantly, the final listwise ensemble step was crucial, whereas calibration helped only in creating diversity.},
  pdf = {http://jmlr.org/proceedings/papers/v14/busa-fekete11a/busa-fekete11a.pdf},
}

Fix broken unicode in author names
Fix broken unicode in author names
