| Bacterial evolution of antibiotic hypersensitivity V Lázár, G Pal Singh, R Spohn, I Nagy, B Horváth, M Hrtyan, ... Molecular systems biology 9 (1), 700, 2013 | 378 | 2013 |
| Antagonism between bacteriostatic and bactericidal antibiotics is prevalent PS Ocampo, V Lázár, B Papp, M Arnoldini, P Abel zur Wiesch, ... Antimicrobial agents and chemotherapy 58 (8), 4573-4582, 2014 | 358 | 2014 |
| Genome-wide analysis captures the determinants of the antibiotic cross-resistance interaction network V Lázár, I Nagy, R Spohn, B Csörgő, Á Györkei, Á Nyerges, B Horváth, ... Nature communications 5 (1), 4352, 2014 | 251 | 2014 |
| State-of-the-art anonymization of medical records using an iterative machine learning framework G Szarvas, R Farkas, R Busa-Fekete Journal of the American Medical Informatics Association 14 (5), 574-580, 2007 | 199 | 2007 |
| Extreme f-measure maximization using sparse probability estimates K Jasinska, K Dembczynski, R Busa-Fekete, K Pfannschmidt, T Klerx, ... International conference on machine learning, 1435-1444, 2016 | 131 | 2016 |
| Gossip-based distributed stochastic bandit algorithms B Szorenyi, R Busa-Fekete, I Hegedus, R Ormándi, M Jelasity, B Kégl International conference on machine learning, 19-27, 2013 | 129 | 2013 |
| Boosting products of base classifiers B Kégl, R Busa-Fekete Proceedings of the 26th annual international conference on machine learning …, 2009 | 126 | 2009 |
| Preference-based online learning with dueling bandits: A survey V Bengs, R Busa-Fekete, A El Mesaoudi-Paul, E Hüllermeier Journal of Machine Learning Research 22 (7), 1-108, 2021 | 121 | 2021 |
| MultiBoost: a multi-purpose boosting package D Benbouzid, R Busa-Fekete, N Casagrande, FD Collin, B Kégl The Journal of Machine Learning Research 13 (1), 549-553, 2012 | 117 | 2012 |
| A no-regret generalization of hierarchical softmax to extreme multi-label classification M Wydmuch, K Jasinska, M Kuznetsov, R Busa-Fekete, K Dembczynski Advances in neural information processing systems 31, 2018 | 114 | 2018 |
| Online rank elicitation for plackett-luce: A dueling bandits approach B Szörényi, R Busa-Fekete, A Paul, E Hüllermeier Advances in neural information processing systems 28, 2015 | 104 | 2015 |
| Top-k selection based on adaptive sampling of noisy preferences R Busa-Fekete, B Szorenyi, W Cheng, P Weng, E Hüllermeier International Conference on Machine Learning, 1094-1102, 2013 | 96 | 2013 |
| Fast classification using sparse decision DAGs D Benbouzid, R Busa-Fekete, B Kégl arXiv preprint arXiv:1206.6387, 2012 | 84 | 2012 |
| Preference-based rank elicitation using statistical models: The case of mallows R Busa-Fekete, E Hüllermeier, B Szörényi International conference on machine learning, 1071-1079, 2014 | 83 | 2014 |
| Preference-based reinforcement learning: evolutionary direct policy search using a preference-based racing algorithm R Busa-Fekete, B Szörényi, P Weng, W Cheng, E Hüllermeier Machine learning 97, 327-351, 2014 | 78 | 2014 |
| A survey of preference-based online learning with bandit algorithms R Busa-Fekete, E Hüllermeier Algorithmic Learning Theory: 25th International Conference, ALT 2014, Bled …, 2014 | 67 | 2014 |
| Assessing the degree of nativeness and Parkinson's condition using Gaussian processes and deep rectifier neural networks T Grósz, R Busa-Fekete, G Gosztolya, L Tóth Sixteenth Annual Conference of the International Speech Communication …, 2015 | 65 | 2015 |
| Qualitative multi-armed bandits: A quantile-based approach B Szorenyi, R Busa-Fekete, P Weng, E Hüllermeier International Conference on Machine Learning, 1660-1668, 2015 | 60 | 2015 |
| Fast boosting using adversarial bandits R Busa-Fekete, B Kégl 27th International Conference on Machine Learning (ICML 2010), 143-150, 2010 | 60 | 2010 |
| Detecting autism, emotions and social signals using adaboost. G Gosztolya, R Busa-Fekete, L Tóth INTERSPEECH, 220-224, 2013 | 59 | 2013 |
Balazs KEGLResearch scientist, Paris AI leader, Noah's Ark Lab, Huawei ResearchVerified email at huawei.com