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ORCIDBalázs Kégl
Person information
- affiliation: University of Paris-Saclay, Center for Data Science
- affiliation: University of Paris-Sud, Linear Accelerator Laboratory (LAL)
- affiliation: University of Montreal, Department of Computer Science
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Books and Theses
- 2011
[b1]Balázs Kégl:
Contributions to machine learning: the unsupervised, the supervised, and the Bayesian. University of Paris-Sud, Orsay, France, 2011
Journal Articles
- 2022
- [j14]Nicolas Traut, Katja Heuer
, Guillaume Lemaître, Anita Beggiato, David Germanaud, Monique Elmaleh, Alban Bethegnies, Laurent Bonnasse-Gahot, Weidong Cai, Stanislas Chambon, Freddy Cliquet, Ayoub Ghriss, Nicolas Guigui, Amicie de Pierrefeu, Meng Wang, Valentina Zantedeschi, Alexandre Boucaud, Joris Van den Bossche, Balázs Kégl, Richard Delorme, Thomas Bourgeron, Roberto Toro, Gaël Varoquaux:
Insights from an autism imaging biomarker challenge: Promises and threats to biomarker discovery. NeuroImage 255: 119171 (2022) - 2020
- [j13]Sophie Giffard-Roisin, Mo Yang, Guillaume Charpiat, Christina Kumler-Bonfanti, Balázs Kégl, Claire Monteleoni:
Tropical Cyclone Track Forecasting Using Fused Deep Learning From Aligned Reanalysis Data. Frontiers Big Data 3: 1 (2020) - 2018
- [j12]Patricio Cerda, Gaël Varoquaux, Balázs Kégl:
Similarity encoding for learning with dirty categorical variables. Mach. Learn. 107(8-10): 1477-1494 (2018) - 2013
- [j11]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
- [j10]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) - 2010
- [j9]Julien Perez, Cécile Germain-Renaud, Balázs Kégl, Charles Loomis:
Multi-objective Reinforcement Learning for Responsive Grids. J. Grid Comput. 8(3): 473-492 (2010) - [j8]Guangyi Chen, Balázs Kégl:
Invariant pattern recognition using contourlets and AdaBoost. Pattern Recognit. 43(3): 579-583 (2010) - 2007
- [j7]Sébastien Gambs, Balázs Kégl, Esma Aïmeur:
Privacy-preserving boosting. Data Min. Knowl. Discov. 14(1): 131-170 (2007) - [j6]Guangyi Chen, Balázs Kégl:
Image denoising with complex ridgelets. Pattern Recognit. 40(2): 578-585 (2007) - 2006
- [j5]James Bergstra, Norman Casagrande, Dumitru Erhan, Douglas Eck, Balázs Kégl:
Aggregate features and ADABOOSTfor music classification. Mach. Learn. 65(2-3): 473-484 (2006) - 2002
- [j4]András Antos, Balázs Kégl, Tamás Linder, Gábor Lugosi:
Data-dependent margin-based generalization bounds for classification. J. Mach. Learn. Res. 3: 73-98 (2002) - [j3]Balázs Kégl, Adam Krzyzak:
Piecewise Linear Skeletonization Using Principal Curves. IEEE Trans. Pattern Anal. Mach. Intell. 24(1): 59-74 (2002) - 2001
- [j2]Adam Krzyzak, Jerzy Z. Sasiadek, Balázs Kégl:
Non-parametric identification of dynamic non-linear systems by a Hermite Series Approach. Int. J. Syst. Sci. 32(10): 1261-1285 (2001) - 2000
- [j1]Balázs Kégl, Adam Krzyzak, Tamás Linder, Kenneth Zeger:
Learning and Design of Principal Curves. IEEE Trans. Pattern Anal. Mach. Intell. 22(3): 281-297 (2000)
Conference and Workshop Papers
- 2021
- [c45]Sarah Kamel, Hartmut Hafermann, Dylan Le Gac, Ludovic Dos Santos, Balázs Kégl, Yann Frignac, Gabriel Charlet:
OSNR prediction for optical links via learned noise figures. ECOC 2021: 1-4 - [c44]Balázs Kégl, Gabriel Hurtado, Albert Thomas:
Model-based micro-data reinforcement learning: what are the crucial model properties and which model to choose? ICLR 2021 - 2017
- [c43]Mehdi Cherti, Balázs Kégl, Akin Kazakçi:
De novo drug design with deep generative models : an empirical study. ICLR (Workshop) 2017 - [c42]Mehdi Cherti, Balázs Kégl, Akin Kazakçi:
Out-of-class novelty generation: an experimental foundation. ICLR (Workshop) 2017 - [c41]Mehdi Cherti, Balázs Kégl, Akin Kazakçi:
Out-of-Class Novelty Generation : An Experimental Foundation. ICTAI 2017: 1312-1319 - 2016
- [c40]Claire Adam-Bourdarios, Glen Cowan, Cécile Germain, Isabelle Guyon, Balázs Kégl, David Rousseau:
How machine learning won the Higgs boson challenge. ESANN 2016 - [c39]Akin Kazakçi, Mehdi Cherti, Balázs Kégl:
Digits that are not: Generating new types through deep neural nets. ICCC 2016: 188-196 - 2014
- [c38]Balázs Kégl:
Open Problem: A (missing) boosting-type convergence result for AdaBoost.MH with factorized multi-class classifiers. COLT 2014: 1268-1275 - [c37]Claire Adam-Bourdarios, Glen Cowan, Cécile Germain, Isabelle Guyon, Balázs Kégl, David Rousseau:
The Higgs boson machine learning challenge. HEPML@NIPS 2014: 19-55 - [c36]Balázs Kégl:
The return of AdaBoost.MH: multi-class Hamming trees. ICLR (Poster) 2014 - [c35]Balázs Kégl:
Correlation-based construction of neighborhood and edge features. ICLR (Workshop Poster) 2014 - 2013
- [c34]Balázs Szörényi, Róbert Busa-Fekete, István Hegedüs, Róbert Ormándi, Márk Jelasity, Balázs Kégl:
Gossip-based distributed stochastic bandit algorithms. ICML (3) 2013: 19-27 - [c33]Rémi Bardenet, Mátyás Brendel, Balázs Kégl, Michèle Sebag:
Collaborative hyperparameter tuning. ICML (2) 2013: 199-207 - 2012
- [c32]István Hegedüs, Róbert Busa-Fekete, Róbert Ormándi, Márk Jelasity, Balázs Kégl:
Peer-to-Peer Multi-class Boosting. Euro-Par 2012: 389-400 - [c31]Róbert Busa-Fekete, Djalel Benbouzid, Balázs Kégl:
Fast classification using sparse decision DAGs. ICML 2012 - [c30]Rémi Bardenet, Olivier Cappé, Gersende Fort, Balázs Kégl:
Adaptive Metropolis with Online Relabeling. AISTATS 2012: 91-99 - 2011
- [c29]James Bergstra, Rémi Bardenet, Yoshua Bengio, Balázs Kégl:
Algorithms for Hyper-Parameter Optimization. NIPS 2011: 2546-2554 - [c28]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 - [c27]Róbert Busa-Fekete, Balázs Kégl, Tamás Éltetö, György Szarvas:
Ranking by calibrated AdaBoost. Yahoo! Learning to Rank Challenge 2011: 37-48 - 2010
- [c26]Rémi Bardenet, Balázs Kégl:
Surrogating the surrogate: accelerating Gaussian-process-based global optimization with a mixture cross-entropy algorithm. ICML 2010: 55-62 - [c25]Róbert Busa-Fekete, Balázs Kégl:
Fast boosting using adversarial bandits. ICML 2010: 143-150 - [c24]Guangyi Chen, Wei-Ping Zhu, Balázs Kégl, Róbert Busa-Fekete:
Palmprint Classification Using Wavelets and AdaBoost. ISNN (2) 2010: 178-183 - 2009
- [c23]Balázs Kégl, Róbert Busa-Fekete:
Boosting products of base classifiers. ICML 2009: 497-504 - [c22]András Bánhalmi, Róbert Busa-Fekete, Balázs Kégl:
A One-Class Classification Approach for Protein Sequences and Structures. ISBRA 2009: 310-322 - [c21]Róbert Busa-Fekete, Balázs Kégl:
Accelerating AdaBoost using UCB. KDD Cup 2009: 111-122 - 2008
- [c20]Julien Perez, Cécile Germain-Renaud, Balázs Kégl, Charles Loomis:
Grid Differentiated Services: A Reinforcement Learning Approach. CCGRID 2008: 287-294 - [c19]Julien Perez, Cécile Germain-Renaud, Balázs Kégl, Charles Loomis:
Utility-Based Reinforcement Learning for Reactive Grids. ICAC 2008: 205-206 - 2007
- [c18]Nicolas Le Roux, Yoshua Bengio, Pascal Lamblin, Marc Joliveau, Balázs Kégl:
Learning the 2-D Topology of Images. NIPS 2007: 841-848 - [c17]Guangyi Chen, Balázs Kégl:
Palmprint classification using contourlets. SMC 2007: 1003-1007 - [c16]Guangyi Chen, Balázs Kégl:
Feature extraction using Radon, wavelet and fourier transform. SMC 2007: 1020-1025 - 2006
- [c15]Guangyi Chen, Balázs Kégl:
Invariant Radon-Wavelet Packet Signatures for Pattern Recognition. CCECE 2006: 1471-1474 - 2005
- [c14]Norman Casagrande, Douglas Eck, Balázs Kégl:
Geometry in sound: a speech/Music audio Classifier Inspired by an Image Classifier. ICMC 2005 - [c13]Norman Casagrande, Douglas Eck, Balázs Kégl:
Frame-Level Audio Feature Extraction Using AdaBoost. ISMIR 2005: 345-350 - 2004
- [c12]Balázs Kégl:
Generalization Error and Algorithmic Convergence of Median Boosting. NIPS 2004: 657-664 - [c11]Balázs Kégl, Ligen Wang:
Boosting on Manifolds: Adaptive Regularization of Base Classifiers. NIPS 2004: 665-672 - 2003
- [c10]Balázs Kégl:
Robust Regression by Boosting the Median. COLT 2003: 258-272 - 2002
- [c9]Salah Bouktif, Houari A. Sahraoui, Balázs Kégl:
Combining Software Quality Predictive Models: An Evolutionary Approach. ICSM 2002: 385-392 - [c8]Danielle Azar, Doina Precup, Salah Bouktif, Balázs Kégl, Houari A. Sahraoui:
Combining and Adapting Software Quality Predictive Models by Genetic Algorithms. ASE 2002: 285-288 - [c7]Balázs Kégl:
Intrinsic Dimension Estimation Using Packing Numbers. NIPS 2002: 681-688 - 2001
- [c6]Adam Krzyak, Jerzy Z. Sasiadek, Balázs Kégl:
Identification of nonlinear systems by Hermite series approach. CDC 2001: 2143-2144 - [c5]Balázs Kégl, Tamás Linder, Gábor Lugosi:
Data-Dependent Margin-Based Generalization Bounds for Classification. COLT/EuroCOLT 2001: 368-384 - 2000
- [c4]Balázs Kégl, Adam Krzyzak, Heinrich Niemann:
Radial Basis Function Networks and Complexity Regularization in Function Learning and Classification. ICPR 2000: 2081-2086 - [c3]Balázs Kégl, Adam Krzyzak:
Piecewise Linear Skeletonization Using Principal Curves. ICPR 2000: 3135-3138 - 1998
- [c2]Balázs Kégl, Adam Krzyzak, Heinrich Niemann:
Radial basis function networks in nonparametric classification and function learning. ICPR 1998: 565-570 - [c1]Balázs Kégl, Adam Krzyzak, Tamás Linder, Kenneth Zeger:
A Polygonal Line Algorithm for Constructing Principal Curves. NIPS 1998: 501-507
Editorship
- 2005
- [e1]Balázs Kégl, Guy Lapalme:
Advances in Artificial Intelligence, 18th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2005, Victoria, Canada, May 9-11, 2005, Proceedings. Lecture Notes in Computer Science 3501, Springer 2005, ISBN 3-540-25864-7 [contents]
Informal and Other Publications
- 2024
- [i14]Abdelhakim Benechehab, Albert Thomas, Balázs Kégl:
Deep autoregressive density nets vs neural ensembles for model-based offline reinforcement learning. CoRR abs/2402.02858 (2024) - [i13]Abdelhakim Benechehab, Albert Thomas, Giuseppe Paolo, Maurizio Filippone, Balázs Kégl:
A Multi-step Loss Function for Robust Learning of the Dynamics in Model-based Reinforcement Learning. CoRR abs/2402.03146 (2024) - [i12]Giuseppe Paolo, Jonas Gonzalez-Billandon, Balázs Kégl:
A call for embodied AI. CoRR abs/2402.03824 (2024) - 2023
- [i11]Abdelhakim Benechehab, Giuseppe Paolo, Albert Thomas, Maurizio Filippone, Balázs Kégl:
Multi-timestep models for Model-based Reinforcement Learning. CoRR abs/2310.05672 (2023) - [i10]Balázs Kégl:
A systematic study comparing hyperparameter optimization engines on tabular data. CoRR abs/2311.15854 (2023) - 2022
- [i9]Giuseppe Paolo, Jonas Gonzalez-Billandon, Albert Thomas, Balázs Kégl:
Guided Safe Shooting: model based reinforcement learning with safety constraints. CoRR abs/2206.09743 (2022) - 2021
- [i8]Balázs Kégl, Gabriel Hurtado, Albert Thomas:
Model-based micro-data reinforcement learning: what are the crucial model properties and which model to choose? CoRR abs/2107.11587 (2021) - [i7]Aladin Virmaux, Illyyne Saffar, Jianfeng Zhang, Balázs Kégl:
Knothe-Rosenblatt transport for Unsupervised Domain Adaptation. CoRR abs/2110.02716 (2021) - 2019
- [i6]Léonard Boussioux, Tomás Giro-Larraz, Charles Guille-Escuret, Mehdi Cherti, Balázs Kégl:
InsectUp: Crowdsourcing Insect Observations to Assess Demographic Shifts and Improve Classification. CoRR abs/1906.11898 (2019) - [i5]Sophie Giffard-Roisin, Mo Yang, Guillaume Charpiat, Christina Kumler-Bonfanti, Balázs Kégl, Claire Monteleoni:
Tropical Cyclone Track Forecasting using Fused Deep Learning from Aligned Reanalysis Data. CoRR abs/1910.10566 (2019) - 2018
- [i4]Patricio Cerda, Gaël Varoquaux, Balázs Kégl:
Similarity encoding for learning with dirty categorical variables. CoRR abs/1806.00979 (2018) - [i3]Balázs Kégl, Mehdi Cherti, Akin Kazakçi:
Spurious samples in deep generative models: bug or feature? CoRR abs/1810.01876 (2018) - 2017
- [i2]Laetitia Le, Camille Marini, Alexandre Gramfort, David Nguyen, Mehdi Cherti, Sana Tfaili, Ali Tfayli, Arlette Baillet-Guffroy, Patrice Prognon, Pierre Chaminade, Eric Caudron, Balázs Kégl:
Machine learning for classification and quantification of monoclonal antibody preparations for cancer therapy. CoRR abs/1705.07099 (2017) - 2016
- [i1]Akin Kazakçi, Mehdi Cherti, Balázs Kégl:
Digits that are not: Generating new types through deep neural nets. CoRR abs/1606.04345 (2016)
Coauthor Index
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