Kégl, Balázs

31 publications

ICML 2025 AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting Abdelhakim Benechehab, Vasilii Feofanov, Giuseppe Paolo, Albert Thomas, Maurizio Filippone, Balázs Kégl

ICLRW 2025 AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting Abdelhakim Benechehab, Vasilii Feofanov, Giuseppe Paolo, Albert Thomas, Maurizio Filippone, Balázs Kégl

ICLR 2025 Zero-Shot Model-Based Reinforcement Learning Using Large Language Models Abdelhakim Benechehab, Youssef Attia El Hili, Ambroise Odonnat, Oussama Zekri, Albert Thomas, Giuseppe Paolo, Maurizio Filippone, Ievgen Redko, Balázs Kégl

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

ICLR 2021 Model-Based Micro-Data Reinforcement Learning: What Are the Crucial Model Properties and Which Model to Choose? Balázs Kégl, Gabriel Hurtado, Albert Thomas

MLJ 2018 Similarity Encoding for Learning with Dirty Categorical Variables Patricio Cerda, Gaël Varoquaux, Balázs Kégl

ICLR 2017 De Novo Drug Design with Deep Generative Models : An Empirical Study Mehdi Cherti, Balázs Kégl, Akin Kazakçi

ICLR 2017 Out-of-Class Novelty Generation: An Experimental Foundation Mehdi Cherti, Balázs Kégl, Akin Kazakçi

ICLR 2014 Correlation-Based Construction of Neighborhood and Edge Features Balázs Kégl

COLT 2014 Open Problem: A (missing) Boosting-Type Convergence Result for AdaBoost.MH with Factorized Multi-Class Classifiers Balázs Kégl

ICLR 2014 The Return of AdaBoost.MH: Multi-Class Hamming Trees Balázs Kégl

ICML 2013 Collaborative Hyperparameter Tuning Rémi Bardenet, Mátyás Brendel, Balázs Kégl, Michèle Sebag

ICML 2013 Gossip-Based Distributed Stochastic Bandit Algorithms Balazs Szorenyi, Robert Busa-Fekete, Istvan Hegedus, Robert Ormandi, Mark Jelasity, Balazs Kegl

MLJ 2013 Tune and Mix: Learning to Rank Using Ensembles of Calibrated Multi-Class Classifiers Róbert Busa-Fekete, Balázs Kégl, Tamás Éltetö, György Szarvas

AISTATS 2012 Adaptive Metropolis with Online Relabeling Remi Bardenet, Olivier Cappe, Gersende Fort, Balazs Kegl

ICML 2012 Fast Classification Using Sparse Decision DAGs Róbert Busa-Fekete, Djalel Benbouzid, Balázs Kégl

MLOSS 2012 MULTIBOOST: A Multi-Purpose Boosting Package Djalel Benbouzid, Róbert Busa-Fekete, Norman Casagrande, François-David Collin, Balázs Kégl

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

NeurIPS 2011 Algorithms for Hyper-Parameter Optimization James S. Bergstra, Rémi Bardenet, Yoshua Bengio, Balázs Kégl

ICML 2010 Fast Boosting Using Adversarial Bandits Róbert Busa-Fekete, Balázs Kégl

ICML 2010 Surrogating the Surrogate: Accelerating Gaussian-Process-Based Global Optimization with a Mixture Cross-Entropy Algorithm Rémi Bardenet, Balázs Kégl

ICML 2009 Boosting Products of Base Classifiers Balázs Kégl, Róbert Busa-Fekete

NeurIPS 2007 Learning the 2-D Topology of Images Nicolas L. Roux, Yoshua Bengio, Pascal Lamblin, Marc Joliveau, Balázs Kégl

MLJ 2006 Aggregate Features and ADABOOSTfor Music Classification James Bergstra, Norman Casagrande, Dumitru Erhan, Douglas Eck, Balázs Kégl

NeurIPS 2004 Boosting on Manifolds: Adaptive Regularization of Base Classifiers Ligen Wang, Balázs Kégl

NeurIPS 2004 Generalization Error and Algorithmic Convergence of Median Boosting Balázs Kégl

COLT 2003 Robust Regression by Boosting the Median Balázs Kégl

JMLR 2002 Data-Dependent Margin-Based Generalization Bounds for Classification András Antos, Balázs Kégl, Tamás Linder, Gábor Lugosi

NeurIPS 2002 Intrinsic Dimension Estimation Using Packing Numbers Balázs Kégl

COLT 2001 Data-Dependent Margin-Based Generalization Bounds for Classification Balázs Kégl, Tamás Linder, Gábor Lugosi

NeurIPS 1998 A Polygonal Line Algorithm for Constructing Principal Curves Balázs Kégl, Adam Krzyzak, Tamás Linder, Kenneth Zeger