Balázs Kégl

Publication Activity (10 Years)

Years Active: 1998-2024
Publications (10 Years): 24

Top Topics

Confusion Matrices

Reinforcement Learning

Distributed Breakout

Biomarker Discovery

Top Venues

ICLR (Workshop)

ICLR (Workshop Poster)

Publications

Giuseppe Paolo, Jonas Gonzalez-Billandon, Balázs Kégl
A call for embodied AI. CoRR (2024)
Abdelhakim Benechehab, Albert Thomas, Balázs Kégl
Deep autoregressive density nets vs neural ensembles for model-based offline reinforcement learning. CoRR (2024)
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 (2024)
Abdelhakim Benechehab, Giuseppe Paolo, Albert Thomas, Maurizio Filippone, Balázs Kégl
Multi-timestep models for Model-based Reinforcement Learning. CoRR (2023)
Balázs Kégl
A systematic study comparing hyperparameter optimization engines on tabular data. CoRR (2023)
Giuseppe Paolo, Jonas Gonzalez-Billandon, Albert Thomas, Balázs Kégl
Guided Safe Shooting: model based reinforcement learning with safety constraints. CoRR (2022)
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 (2022)
Aladin Virmaux, Illyyne Saffar, Jianfeng Zhang, Balázs Kégl
Knothe-Rosenblatt transport for Unsupervised Domain Adaptation. CoRR (2021)
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)
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)
Balázs Kégl, Gabriel Hurtado, Albert Thomas
Model-based micro-data reinforcement learning: what are the crucial model properties and which model to choose? CoRR (2021)
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 (2020)
Léonard Boussioux, Tomás Giro-Larraz, Charles Guille-Escuret, Mehdi Cherti, Balázs Kégl
InsectUp: Crowdsourcing Insect Observations to Assess Demographic Shifts and Improve Classification. CoRR (2019)
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 (2019)
Patricio Cerda, Gaël Varoquaux, Balázs Kégl
Similarity encoding for learning with dirty categorical variables. CoRR (2018)
Balázs Kégl, Mehdi Cherti, Akin Kazakçi
Spurious samples in deep generative models: bug or feature? CoRR (2018)
Patricio Cerda, Gaël Varoquaux, Balázs Kégl
Similarity encoding for learning with dirty categorical variables. Mach. Learn. 107 (8-10) (2018)
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 (2017)
Mehdi Cherti, Balázs Kégl, Akin Kazakçi
Out-of-Class Novelty Generation : An Experimental Foundation. ICTAI (2017)
Mehdi Cherti, Balázs Kégl, Akin Kazakçi
De novo drug design with deep generative models : an empirical study. ICLR (Workshop) (2017)
Mehdi Cherti, Balázs Kégl, Akin Kazakçi
Out-of-class novelty generation: an experimental foundation. ICLR (Workshop) (2017)
Akin Kazakçi, Mehdi Cherti, Balázs Kégl
Digits that are not: Generating new types through deep neural nets. CoRR (2016)
Claire Adam-Bourdarios, Glen Cowan, Cécile Germain, Isabelle Guyon, Balázs Kégl, David Rousseau
How machine learning won the Higgs boson challenge. ESANN (2016)
Akin Kazakçi, Mehdi Cherti, Balázs Kégl
Digits that are not: Generating new types through deep neural nets. ICCC (2016)
Balázs Kégl
Open Problem: A (missing) boosting-type convergence result for AdaBoost.MH with factorized multi-class classifiers. COLT (2014)
Claire Adam-Bourdarios, Glen Cowan, Cécile Germain, Isabelle Guyon, Balázs Kégl, David Rousseau
The Higgs boson machine learning challenge. HEPML@NIPS (2014)
Balázs Kégl
The return of AdaBoost.MH: multi-class Hamming trees. ICLR (Poster) (2014)
Balázs Kégl
Correlation-based construction of neighborhood and edge features. ICLR (Workshop Poster) (2014)
Rémi Bardenet, Mátyás Brendel, Balázs Kégl, Michèle Sebag
Collaborative hyperparameter tuning. ICML (2) (2013)
Balázs Szörényi, Róbert Busa-Fekete, István Hegedüs, Róbert Ormándi, Márk Jelasity, Balázs Kégl
Gossip-based distributed stochastic bandit algorithms. ICML (3) (2013)
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) (2013)
Rémi Bardenet, Olivier Cappé, Gersende Fort, Balázs Kégl
Adaptive Metropolis with Online Relabeling. AISTATS (2012)
Djalel Benbouzid, Róbert Busa-Fekete, Norman Casagrande, François-David Collin, Balázs Kégl
MULTIBOOST: A Multi-purpose Boosting Package. J. Mach. Learn. Res. 13 (2012)
István Hegedüs, Róbert Busa-Fekete, Róbert Ormándi, Márk Jelasity, Balázs Kégl
Peer-to-Peer Multi-class Boosting. Euro-Par (2012)
Róbert Busa-Fekete, Djalel Benbouzid, Balázs Kégl
Fast classification using sparse decision DAGs. ICML (2012)
Róbert Busa-Fekete, Balázs Kégl, Tamás Éltetö, György Szarvas
A Robust Ranking Methodology Based on Diverse Calibration of AdaBoost. ECML/PKDD (1) (2011)
Róbert Busa-Fekete, Balázs Kégl, Tamás Éltetö, György Szarvas
Ranking by calibrated AdaBoost. Yahoo! Learning to Rank Challenge (2011)
James Bergstra, Rémi Bardenet, Yoshua Bengio, Balázs Kégl
Algorithms for Hyper-Parameter Optimization. NIPS (2011)
Rémi Bardenet, Balázs Kégl
Surrogating the surrogate: accelerating Gaussian-process-based global optimization with a mixture cross-entropy algorithm. ICML (2010)
Julien Perez, Cécile Germain-Renaud, Balázs Kégl, Charles Loomis
Multi-objective Reinforcement Learning for Responsive Grids. J. Grid Comput. 8 (3) (2010)
Guangyi Chen, Wei-Ping Zhu, Balázs Kégl, Róbert Busa-Fekete
Palmprint Classification Using Wavelets and AdaBoost. ISNN (2) (2010)
Róbert Busa-Fekete, Balázs Kégl
Fast boosting using adversarial bandits. ICML (2010)
Guangyi Chen, Balázs Kégl
Invariant pattern recognition using contourlets and AdaBoost. Pattern Recognit. 43 (3) (2010)
Balázs Kégl, Róbert Busa-Fekete
Boosting products of base classifiers. ICML (2009)
Róbert Busa-Fekete, Balázs Kégl
Accelerating AdaBoost using UCB. KDD Cup (2009)
András Bánhalmi, Róbert Busa-Fekete, Balázs Kégl
A One-Class Classification Approach for Protein Sequences and Structures. ISBRA (2009)
Julien Perez, Cécile Germain-Renaud, Balázs Kégl, Charles Loomis
Utility-Based Reinforcement Learning for Reactive Grids. ICAC (2008)
Julien Perez, Cécile Germain-Renaud, Balázs Kégl, Charles Loomis
Grid Differentiated Services: A Reinforcement Learning Approach. CCGRID (2008)
Nicolas Le Roux, Yoshua Bengio, Pascal Lamblin, Marc Joliveau, Balázs Kégl
Learning the 2-D Topology of Images. NIPS (2007)
Sébastien Gambs, Balázs Kégl, Esma Aïmeur
Privacy-preserving boosting. Data Min. Knowl. Discov. 14 (1) (2007)
Guangyi Chen, Balázs Kégl
Palmprint classification using contourlets. SMC (2007)
Guangyi Chen, Balázs Kégl
Image denoising with complex ridgelets. Pattern Recognit. 40 (2) (2007)
Guangyi Chen, Balázs Kégl
Feature extraction using Radon, wavelet and fourier transform. SMC (2007)
Guangyi Chen, Balázs Kégl
Invariant Radon-Wavelet Packet Signatures for Pattern Recognition. CCECE (2006)
James Bergstra, Norman Casagrande, Dumitru Erhan, Douglas Eck, Balázs Kégl
Aggregate features and ADABOOSTfor music classification. Mach. Learn. 65 (2-3) (2006)
Norman Casagrande, Douglas Eck, Balázs Kégl
Geometry in sound: a speech/Music audio Classifier Inspired by an Image Classifier. ICMC (2005)
Norman Casagrande, Douglas Eck, Balázs Kégl
Frame-Level Audio Feature Extraction Using AdaBoost. ISMIR (2005)
Balázs Kégl, Ligen Wang
Boosting on Manifolds: Adaptive Regularization of Base Classifiers. NIPS (2004)
Balázs Kégl
Generalization Error and Algorithmic Convergence of Median Boosting. NIPS (2004)
Balázs Kégl
Robust Regression by Boosting the Median. COLT (2003)
Balázs Kégl, Adam Krzyzak
Piecewise Linear Skeletonization Using Principal Curves. IEEE Trans. Pattern Anal. Mach. Intell. 24 (1) (2002)
Salah Bouktif, Houari A. Sahraoui, Balázs Kégl
Combining Software Quality Predictive Models: An Evolutionary Approach. ICSM (2002)
Balázs Kégl
Intrinsic Dimension Estimation Using Packing Numbers. NIPS (2002)
András Antos, Balázs Kégl, Tamás Linder, Gábor Lugosi
Data-dependent margin-based generalization bounds for classification. J. Mach. Learn. Res. 3 (2002)
Danielle Azar, Doina Precup, Salah Bouktif, Balázs Kégl, Houari A. Sahraoui
Combining and Adapting Software Quality Predictive Models by Genetic Algorithms. ASE (2002)
Balázs Kégl, Tamás Linder, Gábor Lugosi
Data-Dependent Margin-Based Generalization Bounds for Classification. COLT/EuroCOLT (2001)
Adam Krzyak, Jerzy Z. Sasiadek, Balázs Kégl
Identification of nonlinear systems by Hermite series approach. CDC (2001)
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) (2001)
Balázs Kégl, Adam Krzyzak, Heinrich Niemann
Radial Basis Function Networks and Complexity Regularization in Function Learning and Classification. ICPR (2000)
Balázs Kégl, Adam Krzyzak, Tamás Linder, Kenneth Zeger
Learning and Design of Principal Curves. IEEE Trans. Pattern Anal. Mach. Intell. 22 (3) (2000)
Balázs Kégl, Adam Krzyzak
Piecewise Linear Skeletonization Using Principal Curves. ICPR (2000)
Balázs Kégl, Adam Krzyzak, Tamás Linder, Kenneth Zeger
A Polygonal Line Algorithm for Constructing Principal Curves. NIPS (1998)
Balázs Kégl, Adam Krzyzak, Heinrich Niemann
Radial basis function networks in nonparametric classification and function learning. ICPR (1998)