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Balázs Kégl
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- 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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2020 – today
- 2024
- [c46]Giuseppe Paolo, Jonas Gonzalez-Billandon, Balázs Kégl:
Position: A Call for Embodied AI. ICML 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
- [j14]Nicolas Traut, Katja Heuer
, Guillaume Lemaître, Anita Beggiato, David Germanaud
Insights from an autism imaging biomarker challenge: Promises and threats to biomarker discovery. NeuroImage 255: 119171 (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
- [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 - [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) - 2020
- [j13]Sophie Giffard-Roisin
Tropical Cyclone Track Forecasting Using Fused Deep Learning From Aligned Reanalysis Data. Frontiers Big Data 3: 1 (2020)
2010 – 2019
- 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
Tropical Cyclone Track Forecasting using Fused Deep Learning from Aligned Reanalysis Data. CoRR abs/1910.10566 (2019) - 2018
- [j12]Patricio Cerda
Similarity encoding for learning with dirty categorical variables. Mach. Learn. 107(8-10): 1477-1494 (2018) - [d1]Nicolas Traut
IMaging-PsychiAtry Challenge rfMRI data. Zenodo, 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
- [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 - [i2]Laetitia Le, Camille Marini, Alexandre Gramfort, David Nguyen, Mehdi Cherti, Sana Tfaili
Machine learning for classification and quantification of monoclonal antibody preparations for cancer therapy. CoRR abs/1705.07099 (2017) - 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 - [i1]Akin Kazakçi, Mehdi Cherti, Balázs Kégl:
Digits that are not: Generating new types through deep neural nets. CoRR abs/1606.04345 (2016) - 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
- [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) - [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
- [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) - [c32]István Hegedüs, Róbert Busa-Fekete, Róbert Ormándi, Márk Jelasity
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
- [b1]Balázs Kégl:
Contributions to machine learning: the unsupervised, the supervised, and the Bayesian. University of Paris-Sud, Orsay, France, 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
- [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) - [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
Palmprint Classification Using Wavelets and AdaBoost. ISNN (2) 2010: 178-183
2000 – 2009
- 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
- [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) - [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
- [j5]James Bergstra, Norman Casagrande, Dumitru Erhan
Aggregate features and ADABOOSTfor music classification. Mach. Learn. 65(2-3): 473-484 (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 - [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] - 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
- [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) - [c9]Salah Bouktif, Houari A. Sahraoui, Balázs Kégl:
Combining Software Quality Predictive Models: An Evolutionary Approach. ICSM 2002: 385-392 - [c8]Danielle Azar
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
- [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) - [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
- [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) - [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
1990 – 1999
- 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
Coauthor Index
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