Radial basis function networks in nonparametric classification and function learning. - Balázs Kégl; Adam Krzyzak; Heinrich Niemann (ICPR, 1998)

A Polygonal Line Algorithm for Constructing Principal Curves. - Balázs Kégl; Adam Krzyzak; Tamás Linder; Kenneth Zeger (NIPS, 1998)

Radial Basis Function Networks and Complexity Regularization in Function Learning and Classification. - Balázs Kégl; Adam Krzyzak; Heinrich Niemann (ICPR, 2000)

Learning and Design of Principal Curves. - Balázs Kégl; Adam Krzyzak; Tamás Linder; Kenneth Zeger (IEEE Trans. Pattern Anal. Mach. Intell., 2000)

Identification of nonlinear systems by Hermite series approach. - Adam Krzyak; Jerzy Z. Sasiadek; Balázs Kégl (CDC, 2001)

Data-Dependent Margin-Based Generalization Bounds for Classification. - Balázs Kégl; Tamás Linder; Gábor Lugosi (COLT/EuroCOLT, 2001)

Non-parametric identification of dynamic non-linear systems by a Hermite Series Approach. - Adam Krzyzak; Jerzy Z. Sasiadek; Balázs Kégl (Int. J. Syst. Sci., 2001)

Combining Software Quality Predictive Models: An Evolutionary Approach. - Salah Bouktif; Houari A. Sahraoui; Balázs Kégl (ICSM, 2002)

Combining and Adapting Software Quality Predictive Models by Genetic Algorithms. - Danielle Azar; Doina Precup; Salah Bouktif; Balázs Kégl; Houari A. Sahraoui (ASE, 2002)

Intrinsic Dimension Estimation Using Packing Numbers. - Balázs Kégl (NIPS, 2002)

Data-dependent margin-based generalization bounds for classification. - András Antos; Balázs Kégl; Tamás Linder; Gábor Lugosi (J. Mach. Learn. Res., 2002)

Piecewise Linear Skeletonization Using Principal Curves. - Balázs Kégl; Adam Krzyzak (IEEE Trans. Pattern Anal. Mach. Intell., 2002)

Robust Regression by Boosting the Median. - Balázs Kégl (COLT, 2003)

Generalization Error and Algorithmic Convergence of Median Boosting. - Balázs Kégl (NIPS, 2004)

Boosting on Manifolds: Adaptive Regularization of Base Classifiers. - Balázs Kégl; Ligen Wang (NIPS, 2004)

Geometry in sound: a speech/Music audio Classifier Inspired by an Image Classifier. - Norman Casagrande; Douglas Eck; Balázs Kégl (ICMC, 2005)

Frame-Level Audio Feature Extraction Using AdaBoost. - Norman Casagrande; Douglas Eck; Balázs Kégl (ISMIR, 2005)

Invariant Radon-Wavelet Packet Signatures for Pattern Recognition. - Guangyi Chen 001; Balázs Kégl (CCECE, 2006)

Aggregate features and ADABOOSTfor music classification. - James Bergstra; Norman Casagrande; Dumitru Erhan; Douglas Eck; Balázs Kégl (Mach. Learn., 2006)

Learning the 2-D Topology of Images. - Nicolas Le Roux; Yoshua Bengio; Pascal Lamblin; Marc Joliveau; Balázs Kégl (NIPS, 2007)

Palmprint classification using contourlets. - Guangyi Chen 001; Balázs Kégl (SMC, 2007)

Feature extraction using Radon, wavelet and fourier transform. - Guangyi Chen 001; Balázs Kégl (SMC, 2007)

Privacy-preserving boosting. - Sébastien Gambs; Balázs Kégl; Esma Aïmeur (Data Min. Knowl. Discov., 2007)

Image denoising with complex ridgelets. - Guangyi Chen 001; Balázs Kégl (Pattern Recognit., 2007)

Grid Differentiated Services: A Reinforcement Learning Approach. - Julien Perez; Cécile Germain-Renaud; Balázs Kégl; Charles Loomis (CCGRID, 2008)

Utility-Based Reinforcement Learning for Reactive Grids. - Julien Perez; Cécile Germain-Renaud; Balázs Kégl; Charles Loomis (ICAC, 2008)

Boosting products of base classifiers. - Balázs Kégl; Róbert Busa-Fekete (ICML, 2009)

A One-Class Classification Approach for Protein Sequences and Structures. - András Bánhalmi; Róbert Busa-Fekete; Balázs Kégl (ISBRA, 2009)

Accelerating AdaBoost using UCB. - Róbert Busa-Fekete; Balázs Kégl (KDD Cup, 2009)

Surrogating the surrogate: accelerating Gaussian-process-based global optimization with a mixture cross-entropy algorithm. - Rémi Bardenet; Balázs Kégl (ICML, 2010)

Fast boosting using adversarial bandits. - Róbert Busa-Fekete; Balázs Kégl (ICML, 2010)

Palmprint Classification Using Wavelets and AdaBoost. - Guangyi Chen 001; Wei-Ping Zhu 001; Balázs Kégl; Róbert Busa-Fekete (ISNN (2), 2010)

Multi-objective Reinforcement Learning for Responsive Grids. - Julien Perez; Cécile Germain-Renaud; Balázs Kégl; Charles Loomis (J. Grid Comput., 2010)

Invariant pattern recognition using contourlets and AdaBoost. - Guangyi Chen 001; Balázs Kégl (Pattern Recognit., 2010)

Contributions to machine learning: the unsupervised, the supervised, and the Bayesian. - Balázs Kégl (2011)

Algorithms for Hyper-Parameter Optimization. - James Bergstra; Rémi Bardenet; Yoshua Bengio; Balázs Kégl (NIPS, 2011)

A Robust Ranking Methodology Based on Diverse Calibration of AdaBoost. - Róbert Busa-Fekete; Balázs Kégl; Tamás Éltetö; György Szarvas (ECML/PKDD (1), 2011)

Ranking by calibrated AdaBoost. - Róbert Busa-Fekete; Balázs Kégl; Tamás Éltetö; György Szarvas (Yahoo! Learning to Rank Challenge, 2011)

Peer-to-Peer Multi-class Boosting. - István Hegedüs; Róbert Busa-Fekete; Róbert Ormándi; Márk Jelasity; Balázs Kégl (Euro-Par, 2012)

Fast classification using sparse decision DAGs. - Róbert Busa-Fekete; Djalel Benbouzid; Balázs Kégl (ICML, 2012)

Adaptive Metropolis with Online Relabeling. - Rémi Bardenet; Olivier Cappé; Gersende Fort; Balázs Kégl (AISTATS, 2012)

MULTIBOOST: A Multi-purpose Boosting Package. - Djalel Benbouzid; Róbert Busa-Fekete; Norman Casagrande; François-David Collin; Balázs Kégl (J. Mach. Learn. Res., 2012)

Collaborative hyperparameter tuning. - Rémi Bardenet; Mátyás Brendel; Balázs Kégl; Michèle Sebag (ICML (2), 2013)

Gossip-based distributed stochastic bandit algorithms. - Balázs Szörényi; Róbert Busa-Fekete; István Hegedüs; Róbert Ormándi; Márk Jelasity; Balázs Kégl (ICML (3), 2013)

Tune and mix: learning to rank using ensembles of calibrated multi-class classifiers. - Róbert Busa-Fekete; Balázs Kégl; Tamás Éltetö; György Szarvas (Mach. Learn., 2013)

Open Problem: A (missing) boosting-type convergence result for AdaBoost.MH with factorized multi-class classifiers. - Balázs Kégl (COLT, 2014)

The Higgs boson machine learning challenge. - Claire Adam-Bourdarios; Glen Cowan; Cécile Germain; Isabelle Guyon; Balázs Kégl; David Rousseau (HEPML@NIPS, 2014)

The return of AdaBoost.MH: multi-class Hamming trees. - Balázs Kégl (ICLR (Poster), 2014)

Correlation-based construction of neighborhood and edge features. - Balázs Kégl (ICLR (Workshop Poster), 2014)

How machine learning won the Higgs boson challenge. - Claire Adam-Bourdarios; Glen Cowan; Cécile Germain; Isabelle Guyon; Balázs Kégl; David Rousseau (ESANN, 2016)

Digits that are not: Generating new types through deep neural nets. - Akin Kazakçi; Mehdi Cherti; Balázs Kégl (ICCC, 2016)

De novo drug design with deep generative models : an empirical study. - Mehdi Cherti; Balázs Kégl; Akin Kazakçi (ICLR (Workshop), 2017)

Out-of-class novelty generation: an experimental foundation. - Mehdi Cherti; Balázs Kégl; Akin Kazakçi (ICLR (Workshop), 2017)

Machine learning for classification and quantification of monoclonal antibody preparations for cancer therapy. - 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 (CoRR, 2017)

Spurious samples in deep generative models: bug or feature? - Balázs Kégl; Mehdi Cherti; Akin Kazakçi (CoRR, 2018)

Similarity encoding for learning with dirty categorical variables. - Patricio Cerda; Gaël Varoquaux; Balázs Kégl (Mach. Learn., 2018)

IMaging-PsychiAtry Challenge rfMRI data. - Nicolas Traut; Guillaume Lemaître; Katja Heuer; Anita Beggiato; Balázs Kégl; Richard Delorme; Thomas Bourgeron; Roberto Toro; Gaël Varoquaux (Zenodo, 2018)

InsectUp: Crowdsourcing Insect Observations to Assess Demographic Shifts and Improve Classification. - Léonard Boussioux; Tomás Giro-Larraz; Charles Guille-Escuret; Mehdi Cherti; Balázs Kégl (CoRR, 2019)

Tropical Cyclone Track Forecasting Using Fused Deep Learning From Aligned Reanalysis Data. - Sophie Giffard-Roisin; Mo Yang; Guillaume Charpiat; Christina Kumler-Bonfanti; Balázs Kégl; Claire Monteleoni (Frontiers Big Data, 2020)

OSNR prediction for optical links via learned noise figures. - Sarah Kamel; Hartmut Hafermann; Dylan Le Gac; Ludovic Dos Santos; Balázs Kégl; Yann Frignac; Gabriel Charlet (ECOC, 2021)

Model-based micro-data reinforcement learning: what are the crucial model properties and which model to choose? - Balázs Kégl; Gabriel Hurtado; Albert Thomas 001 (ICLR, 2021)

Knothe-Rosenblatt transport for Unsupervised Domain Adaptation. - Aladin Virmaux; Illyyne Saffar; Jianfeng Zhang; Balázs Kégl (CoRR, 2021)

Guided Safe Shooting: model based reinforcement learning with safety constraints. - Giuseppe Paolo; Jonas Gonzalez-Billandon; Albert Thomas 001; Balázs Kégl (CoRR, 2022)

Insights from an autism imaging biomarker challenge: Promises and threats to biomarker discovery. - Nicolas Traut; Katja Heuer; Guillaume Lemaître; Anita Beggiato; David Germanaud; Monique Elmaleh; Alban Bethegnies; Laurent Bonnasse-Gahot; Weidong Cai 002; Stanislas Chambon; Freddy Cliquet; Ayoub Ghriss; et al. (NeuroImage, 2022)

Multi-timestep models for Model-based Reinforcement Learning. - Abdelhakim Benechehab; Giuseppe Paolo; Albert Thomas 001; Maurizio Filippone; Balázs Kégl (CoRR, 2023)

A systematic study comparing hyperparameter optimization engines on tabular data. - Balázs Kégl (CoRR, 2023)

Position: A Call for Embodied AI. - Giuseppe Paolo; Jonas Gonzalez-Billandon; Balázs Kégl (ICML, 2024)

Deep autoregressive density nets vs neural ensembles for model-based offline reinforcement learning. - Abdelhakim Benechehab; Albert Thomas 001; Balázs Kégl (CoRR, 2024)

A Multi-step Loss Function for Robust Learning of the Dynamics in Model-based Reinforcement Learning. - Abdelhakim Benechehab; Albert Thomas 001; Giuseppe Paolo; Maurizio Filippone; Balázs Kégl (CoRR, 2024)

A call for embodied AI. - Giuseppe Paolo; Jonas Gonzalez-Billandon; Balázs Kégl (CoRR, 2024)

Large Language Models Orchestrating Structured Reasoning Achieve Kaggle Grandmaster Level. - Antoine Grosnit; Alexandre Maraval; James Doran; Giuseppe Paolo; Albert Thomas 001; Refinath Shahul Hameed Nabeezath Beevi; Jonas Gonzalez; Khyati Khandelwal; Ignacio Iacobacci; Abdelhakim Benechehab; Hamza Cherkaoui; Youssef Attia El Hili; et al. (CoRR, 2024)

Zero-shot Model-based Reinforcement Learning using Large Language Models. - Abdelhakim Benechehab; Youssef Attia El Hili; Ambroise Odonnat; Oussama Zekri; Albert Thomas 001; Giuseppe Paolo; Maurizio Filippone; Ievgen Redko; Balázs Kégl (ICLR, 2025)

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting. - Abdelhakim Benechehab; Vasilii Feofanov; Giuseppe Paolo; Albert Thomas 001; Maurizio Filippone; Balázs Kégl (CoRR, 2025)

TAG: A Decentralized Framework for Multi-Agent Hierarchical Reinforcement Learning. - Giuseppe Paolo; Abdelhakim Benechehab; Hamza Cherkaoui; Albert Thomas 001; Balázs Kégl (CoRR, 2025)

High-Dimensional Analysis of Bootstrap Ensemble Classifiers. - Hamza Cherkaoui; Malik Tiomoko; Mohamed El Amine Seddik; Cosme Louart; Ekkehard Schnoor; Balázs Kégl (CoRR, 2025)