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ORCIDRóbert Busa-Fekete
Person information
- affiliation: Google Research, New York, NY, USA
- affiliation: Yahoo Research, New York, NY, USA
- affiliation: University of Paderborn, Department of Computer Science, Paderborn, Germany
- affiliation: University of Marburg, Department of Mathematics and Computer Science, Marburg, Germany
- affiliation: University of Paris-Sud, Linear Accelerator Laboratory (LAL), Orsay, France
- affiliation (PhD 2008): University of Szeged, Szeged, Hungary
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Books and Theses
- 2008
[b1]Róbert Istvan Busa-Fekete:
EVOLUTIONARY TREE RECONSTRUCTION AND ITS APPLICATIONS IN PROTEIN CLASSIFICATION. University of Szeged, Hungary, 2008
Journal Articles
- 2021
- [j10]Viktor Bengs, Róbert Busa-Fekete, Adil El Mesaoudi-Paul, Eyke Hüllermeier:
Preference-based Online Learning with Dueling Bandits: A Survey. J. Mach. Learn. Res. 22: 7:1-7:108 (2021) - [j9]Gábor Gosztolya
, Róbert Busa-Fekete:
Ensemble Bag-of-Audio-Words Representation Improves Paralinguistic Classification Accuracy. IEEE ACM Trans. Audio Speech Lang. Process. 29: 477-488 (2021) - 2019
- [j8]Gábor Gosztolya, Róbert Busa-Fekete:
Calibrating AdaBoost for phoneme classification. Soft Comput. 23(1): 115-128 (2019) - 2014
- [j7]Róbert Busa-Fekete, Balázs Szörényi, Paul Weng, Weiwei Cheng, Eyke Hüllermeier:
Preference-based reinforcement learning: evolutionary direct policy search using a preference-based racing algorithm. Mach. Learn. 97(3): 327-351 (2014) - 2013
- [j6]Róbert Busa-Fekete, Balázs Kégl, Tamás Éltetö, György Szarvas:
Tune and mix: learning to rank using ensembles of calibrated multi-class classifiers. Mach. Learn. 93(2-3): 261-292 (2013) - 2012
- [j5]Djalel Benbouzid, Róbert Busa-Fekete, Norman Casagrande, François-David Collin, Balázs Kégl:
MULTIBOOST: A Multi-purpose Boosting Package. J. Mach. Learn. Res. 13: 549-553 (2012) - 2009
- [j4]Clifford S. Todd, Tivadar M. Toth, Róbert Busa-Fekete:
GraphClus, a MATLAB program for cluster analysis using graph theory. Comput. Geosci. 35(6): 1205-1213 (2009) - [j3]Richárd Farkas, György Szarvas, István Hegedüs, Attila Almási, Veronika Vincze, Róbert Ormándi, Róbert Busa-Fekete:
Research Paper: Semi-automated Construction of Decision Rules to Predict Morbidities from Clinical Texts. J. Am. Medical Informatics Assoc. 16(4): 601-605 (2009) - 2008
- [j2]Róbert Busa-Fekete, András Kocsor:
Extracting Human Protein Information from MEDLINE Using a Full-Sentence Parser. Acta Cybern. 18(3): 391-402 (2008) - 2007
- [j1]György Szarvas, Richárd Farkas, Róbert Busa-Fekete:
Research Paper: State-of-the-art Anonymization of Medical Records Using an Iterative Machine Learning Framework. J. Am. Medical Informatics Assoc. 14(5): 574-580 (2007)
Conference and Workshop Papers
- 2024
- [c46]Enrico Bacis, Igor Bilogrevic, Róbert Busa-Fekete, Asanka Herath, Antonio Sartori, Umar Syed:
Assessing Web Fingerprinting Risk. WWW (Companion Volume) 2024: 245-254 - 2023
- [c45]Róbert Istvan Busa-Fekete, Andrés Muñoz Medina, Umar Syed, Sergei Vassilvitskii:
Label differential privacy and private training data release. ICML 2023: 3233-3251 - [c44]Róbert Busa-Fekete, Heejin Choi, Travis Dick, Claudio Gentile, Andrés Muñoz Medina:
Easy Learning from Label Proportions. NeurIPS 2023 - 2022
- [c43]Gecia Bravo Hermsdorff, Róbert Busa-Fekete, Mohammad Ghavamzadeh, Andrés Muñoz Medina, Umar Syed:
Private and Communication-Efficient Algorithms for Entropy Estimation. NeurIPS 2022 - [c42]Róbert Busa-Fekete, Heejin Choi, Krzysztof Dembczynski, Claudio Gentile, Henry Reeve, Balázs Szörényi:
Regret Bounds for Multilabel Classification in Sparse Label Regimes. NeurIPS 2022 - [c41]José Vicente Egas López, Róbert Busa-Fekete, Gábor Gosztolya:
On the Use of Ensemble X-Vector Embeddings for Improved Sleepiness Detection. SPECOM 2022: 178-187 - 2021
- [c40]Alessandro Epasto, Andrés Muñoz Medina, Steven Avery, Yijian Bai, Róbert Busa-Fekete, CJ Carey, Ya Gao, David Guthrie, Subham Ghosh, James Ioannidis, Junyi Jiao, Jakub Lacki, Jason Lee, Arne Mauser, Brian Milch, Vahab S. Mirrokni, Deepak Ravichandran, Wei Shi, Max Spero, Yunting Sun, Umar Syed, Sergei Vassilvitskii, Shuo Wang:
Clustering for Private Interest-based Advertising. KDD 2021: 2802-2810 - [c39]Róbert Busa-Fekete, Dimitris Fotakis, Emmanouil Zampetakis:
Private and Non-private Uniformity Testing for Ranking Data. NeurIPS 2021: 9480-9492 - [c38]Róbert Busa-Fekete, Dimitris Fotakis, Balázs Szörényi, Emmanouil Zampetakis:
Identity testing for Mallows model. NeurIPS 2021: 23179-23190 - 2020
- [c37]Utkarsh Upadhyay, Róbert Busa-Fekete, Wojciech Kotlowski, Dávid Pál, Balázs Szörényi:
Learning to Crawl. AAAI 2020: 6046-6053 - 2019
- [c36]Róbert Busa-Fekete, Dimitris Fotakis, Balázs Szörényi, Manolis Zampetakis:
Optimal Learning of Mallows Block Model. COLT 2019: 529-532 - 2018
- [c35]Adil El Mesaoudi-Paul, Eyke Hüllermeier, Róbert Busa-Fekete:
Ranking Distributions based on Noisy Sorting. ICML 2018: 3469-3477 - [c34]Ashok Cutkosky, Róbert Busa-Fekete:
Distributed Stochastic Optimization via Adaptive SGD. NeurIPS 2018: 1914-1923 - [c33]Marek Wydmuch, Kalina Jasinska, Mikhail Kuznetsov, Róbert Busa-Fekete, Krzysztof Dembczynski:
A no-regret generalization of hierarchical softmax to extreme multi-label classification. NeurIPS 2018: 6358-6368 - [c32]Gábor Gosztolya, Róbert Busa-Fekete:
Posterior Calibration for Multi-Class Paralinguistic Classification. SLT 2018: 119-125 - [c31]Akshay Soni, Aasish Pappu, Róbert Busa-Fekete, Krzysztof Dembczynski:
Extreme Multilabel Classification for Social Media Chairs' Welcome and Organization. WWW (Companion Volume) 2018: 1893-1894 - 2017
- [c30]Róbert Busa-Fekete, Balázs Szörényi, Paul Weng, Shie Mannor:
Multi-objective Bandits: Optimizing the Generalized Gini Index. ICML 2017: 625-634 - [c29]Gábor Gosztolya, Róbert Busa-Fekete, Tamás Grósz, László Tóth:
DNN-Based Feature Extraction and Classifier Combination for Child-Directed Speech, Cold and Snoring Identification. INTERSPEECH 2017: 3522-3526 - 2016
- [c28]Kalina Jasinska, Krzysztof Dembczynski, Róbert Busa-Fekete, Karlson Pfannschmidt, Timo Klerx, Eyke Hüllermeier:
Extreme F-measure Maximization using Sparse Probability Estimates. ICML 2016: 1435-1444 - [c27]Gábor Gosztolya, Tamás Grósz, Róbert Busa-Fekete, László Tóth:
Determining Native Language and Deception Using Phonetic Features and Classifier Combination. INTERSPEECH 2016: 2418-2422 - [c26]Krzysztof Dembczynski, Wojciech Kotlowski, Willem Waegeman, Róbert Busa-Fekete, Eyke Hüllermeier:
Consistency of Probabilistic Classifier Trees. ECML/PKDD (2) 2016: 511-526 - 2015
- [c25]Balázs Szörényi, Róbert Busa-Fekete, Paul Weng, Eyke Hüllermeier:
Qualitative Multi-Armed Bandits: A Quantile-Based Approach. ICML 2015: 1660-1668 - [c24]Tamás Grósz, Róbert Busa-Fekete, Gábor Gosztolya, László Tóth:
Assessing the degree of nativeness and parkinson's condition using Gaussian processes and deep rectifier neural networks. INTERSPEECH 2015: 919-923 - [c23]Róbert Busa-Fekete, Balázs Szörényi, Krzysztof Dembczynski, Eyke Hüllermeier:
Online F-Measure Optimization. NIPS 2015: 595-603 - [c22]Balázs Szörényi, Róbert Busa-Fekete, Adil Paul, Eyke Hüllermeier:
Online Rank Elicitation for Plackett-Luce: A Dueling Bandits Approach. NIPS 2015: 604-612 - 2014
- [c21]Róbert Busa-Fekete, Balázs Szörényi, Eyke Hüllermeier:
PAC Rank Elicitation through Adaptive Sampling of Stochastic Pairwise Preferences. AAAI 2014: 1701-1707 - [c20]Róbert Busa-Fekete, Eyke Hüllermeier:
A Survey of Preference-Based Online Learning with Bandit Algorithms. ALT 2014: 18-39 - [c19]Róbert Busa-Fekete, Eyke Hüllermeier, Balázs Szörényi:
Preference-Based Rank Elicitation using Statistical Models: The Case of Mallows. ICML 2014: 1071-1079 - [c18]Gábor Gosztolya, Tamás Grósz, Róbert Busa-Fekete, László Tóth:
Detecting the intensity of cognitive and physical load using AdaBoost and deep rectifier neural networks. INTERSPEECH 2014: 452-456 - 2013
- [c17]György Szarvas, Róbert Busa-Fekete, Eyke Hüllermeier:
Learning to Rank Lexical Substitutions. EMNLP 2013: 1926-1932 - [c16]Balázs Szörényi, Róbert Busa-Fekete, István Hegedüs, Róbert Ormándi, Márk Jelasity, Balázs Kégl:
Gossip-based distributed stochastic bandit algorithms. ICML (3) 2013: 19-27 - [c15]Róbert Busa-Fekete, Balázs Szörényi, Weiwei Cheng, Paul Weng, Eyke Hüllermeier:
Top-k Selection based on Adaptive Sampling of Noisy Preferences. ICML (3) 2013: 1094-1102 - [c14]Gábor Gosztolya, Róbert Busa-Fekete, László Tóth:
Detecting autism, emotions and social signals using adaboost. INTERSPEECH 2013: 220-224 - 2012
- [c13]István Hegedüs, Róbert Busa-Fekete, Róbert Ormándi, Márk Jelasity, Balázs Kégl:
Peer-to-Peer Multi-class Boosting. Euro-Par 2012: 389-400 - [c12]Róbert Busa-Fekete, Djalel Benbouzid, Balázs Kégl:
Fast classification using sparse decision DAGs. ICML 2012 - 2011
- [c11]Róbert Busa-Fekete, Balázs Kégl, Tamás Éltetö, György Szarvas:
A Robust Ranking Methodology Based on Diverse Calibration of AdaBoost. ECML/PKDD (1) 2011: 263-279 - [c10]Róbert Busa-Fekete, Balázs Kégl, Tamás Éltetö, György Szarvas:
Ranking by calibrated AdaBoost. Yahoo! Learning to Rank Challenge 2011: 37-48 - 2010
- [c9]Róbert Busa-Fekete, Balázs Kégl:
Fast boosting using adversarial bandits. ICML 2010: 143-150 - [c8]Guangyi Chen, Wei-Ping Zhu, Balázs Kégl, Róbert Busa-Fekete:
Palmprint Classification Using Wavelets and AdaBoost. ISNN (2) 2010: 178-183 - 2009
- [c7]Balázs Kégl, Róbert Busa-Fekete:
Boosting products of base classifiers. ICML 2009: 497-504 - [c6]András Bánhalmi, Róbert Busa-Fekete, Balázs Kégl:
A One-Class Classification Approach for Protein Sequences and Structures. ISBRA 2009: 310-322 - [c5]Róbert Busa-Fekete, Balázs Kégl:
Accelerating AdaBoost using UCB. KDD Cup 2009: 111-122 - 2007
- [c4]András Bánhalmi, András Kocsor, Róbert Busa-Fekete:
Counter-Example Generation-Based One-Class Classification. ECML 2007: 543-550 - [c3]Róbert Busa-Fekete, András Kocsor, Csaba Bagyinka:
A Multi-Stack Based Phylogenetic Tree Building Method. ISBRA 2007: 49-60 - [c2]András Kocsor, Róbert Busa-Fekete, András Bánhalmi:
Whitening-Based Feature Space Transformations in a Speech Impediment Therapy System. TSD 2007: 222-229 - [c1]András Bánhalmi, Róbert Busa-Fekete, András Kocsor:
An Automatic Retraining Method for Speaker Independent Hidden Markov Models. TSD 2007: 382-389
Parts in Books or Collections
- 2008
- [p1]Róbert Busa-Fekete, András Kocsor, Sándor Pongor:
Tree-Based Algorithms for Protein Classification. Computational Intelligence in Bioinformatics 2008: 165-182
Informal and Other Publications
- 2024
- [i12]Enrico Bacis, Igor Bilogrevic, Róbert Busa-Fekete, Asanka Herath, Antonio Sartori, Umar Syed:
Assessing Web Fingerprinting Risk. CoRR abs/2403.15607 (2024) - 2023
- [i11]Róbert Istvan Busa-Fekete, Heejin Choi, Travis Dick, Claudio Gentile, Andrés Muñoz Medina:
Easy Learning from Label Proportions. CoRR abs/2302.03115 (2023) - [i10]Gecia Bravo Hermsdorff, Róbert Busa-Fekete, Mohammad Ghavamzadeh, Andres Muñoz Medina, Umar Syed:
Private and Communication-Efficient Algorithms for Entropy Estimation. CoRR abs/2305.07751 (2023) - 2022
- [i9]Gecia Bravo Hermsdorff, Róbert Busa-Fekete, Lee M. Gunderson, Andrés Muñoz Medina, Umar Syed:
Statistical anonymity: Quantifying reidentification risks without reidentifying users. CoRR abs/2201.12306 (2022) - 2020
- [i8]Kalina Jasinska-Kobus, Marek Wydmuch, Krzysztof Dembczynski, Mikhail Kuznetsov, Róbert Busa-Fekete:
Probabilistic Label Trees for Extreme Multi-label Classification. CoRR abs/2009.11218 (2020) - 2019
- [i7]Utkarsh Upadhyay, Róbert Busa-Fekete, Wojciech Kotlowski, Dávid Pál, Balázs Szörényi:
Learning to Crawl. CoRR abs/1905.12781 (2019) - [i6]Róbert Busa-Fekete, Krzysztof Dembczynski, Alexander Golovnev, Kalina Jasinska, Mikhail Kuznetsov, Maxim Sviridenko, Chao Xu:
On the computational complexity of the probabilistic label tree algorithms. CoRR abs/1906.00294 (2019) - [i5]Róbert Busa-Fekete, Dimitris Fotakis, Balázs Szörényi, Manolis Zampetakis:
Optimal Learning of Mallows Block Model. CoRR abs/1906.01009 (2019) - 2018
- [i4]Ashok Cutkosky, Róbert Busa-Fekete:
Distributed Stochastic Optimization via Adaptive Stochastic Gradient Descent. CoRR abs/1802.05811 (2018) - [i3]Róbert Busa-Fekete, Eyke Hüllermeier, Adil El Mesaoudi-Paul:
Preference-based Online Learning with Dueling Bandits: A Survey. CoRR abs/1807.11398 (2018) - [i2]Marek Wydmuch, Kalina Jasinska, Mikhail Kuznetsov, Róbert Busa-Fekete, Krzysztof Dembczynski:
A no-regret generalization of hierarchical softmax to extreme multi-label classification. CoRR abs/1810.11671 (2018) - 2017
- [i1]Róbert Busa-Fekete, Balázs Szörényi, Paul Weng, Shie Mannor:
Multi-objective Bandits: Optimizing the Generalized Gini Index. CoRR abs/1706.04933 (2017)
Coauthor Index
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