Balázs Kégl - RankMe

85 papers A* 11A 1B 8C 1Journal 35Unranked 27

Year	Rank	Type	Title / Venue / Authors
2025	A*	`conf`	AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting. ICML Abdelhakim Benechehab, Vasilii Feofanov, Giuseppe Paolo, Albert Thomas, Maurizio Filippone, Balázs Kégl
2025	J	`jnl`	AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting. CoRR Abdelhakim Benechehab, Vasilii Feofanov, Giuseppe Paolo, Albert Thomas, Maurizio Filippone, Balázs Kégl
2025	J	`jnl`	From Data to Rewards: a Bilevel Optimization Perspective on Maximum Likelihood Estimation. CoRR Abdelhakim Benechehab, Gabriel Singer, Corentin Léger, Youssef Attia El Hili, Giuseppe Paolo, Albert Thomas, Maurizio Filippone, Balázs Kégl
2025	J	`jnl`	High-Dimensional Analysis of Bootstrap Ensemble Classifiers. CoRR Hamza Cherkaoui, Malik Tiomoko, Mohamed El Amine Seddik, Cosme Louart, Ekkehard Schnoor, Balázs Kégl
2025	J	`jnl`	LLMs as In-Context Meta-Learners for Model and Hyperparameter Selection. CoRR Youssef Attia El Hili, Albert Thomas, Malik Tiomoko, Abdelhakim Benechehab, Corentin Léger, Corinne Ancourt, Balázs Kégl
2025	J	`jnl`	TAG: A Decentralized Framework for Multi-Agent Hierarchical Reinforcement Learning. CoRR Giuseppe Paolo, Abdelhakim Benechehab, Hamza Cherkaoui, Albert Thomas, Balázs Kégl
2025	A*	`conf`	Zero-shot Model-based Reinforcement Learning using Large Language Models. ICLR Abdelhakim Benechehab, Youssef Attia El Hili, Ambroise Odonnat, Oussama Zekri, Albert Thomas, Giuseppe Paolo, Maurizio Filippone, Ievgen Redko, Balázs Kégl
2024	J	`jnl`	A Multi-step Loss Function for Robust Learning of the Dynamics in Model-based Reinforcement Learning. CoRR Abdelhakim Benechehab, Albert Thomas, Giuseppe Paolo, Maurizio Filippone, Balázs Kégl
2024	J	`jnl`	A call for embodied AI. CoRR Giuseppe Paolo, Jonas Gonzalez-Billandon, Balázs Kégl
2024	J	`jnl`	Deep autoregressive density nets vs neural ensembles for model-based offline reinforcement learning. CoRR Abdelhakim Benechehab, Albert Thomas, Balázs Kégl
2024	J	`jnl`	Large Language Models Orchestrating Structured Reasoning Achieve Kaggle Grandmaster Level. CoRR Antoine Grosnit, Alexandre Maraval, James Doran, Giuseppe Paolo, Albert Thomas, Refinath Shahul Hameed Nabeezath Beevi, Jonas Gonzalez, Khyati Khandelwal, Ignacio Iacobacci, Abdelhakim Benechehab, Hamza Cherkaoui, Youssef Attia El Hili, Kun Shao, Jianye Hao, Jun Yao, Balázs Kégl, Haitham Bou-Ammar, Jun Wang
2024	A*	`conf`	Position: A Call for Embodied AI. ICML Giuseppe Paolo, Jonas Gonzalez-Billandon, Balázs Kégl
2024	J	`jnl`	Zero-shot Model-based Reinforcement Learning using Large Language Models. CoRR Abdelhakim Benechehab, Youssef Attia El Hili, Ambroise Odonnat, Oussama Zekri, Albert Thomas, Giuseppe Paolo, Maurizio Filippone, Ievgen Redko, Balázs Kégl
2023	J	`jnl`	A systematic study comparing hyperparameter optimization engines on tabular data. CoRR Balázs Kégl
2023	J	`jnl`	Multi-timestep models for Model-based Reinforcement Learning. CoRR Abdelhakim Benechehab, Giuseppe Paolo, Albert Thomas, Maurizio Filippone, Balázs Kégl
2022	J	`jnl`	Guided Safe Shooting: model based reinforcement learning with safety constraints. CoRR Giuseppe Paolo, Jonas Gonzalez-Billandon, Albert Thomas, Balázs Kégl
2022	J	`jnl`	Insights from an autism imaging biomarker challenge: Promises and threats to biomarker discovery. NeuroImage 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
2021	J	`jnl`	Knothe-Rosenblatt transport for Unsupervised Domain Adaptation. CoRR Aladin Virmaux, Illyyne Saffar, Jianfeng Zhang, Balázs Kégl
2021	A*	`conf`	Model-based micro-data reinforcement learning: what are the crucial model properties and which model to choose? ICLR Balázs Kégl, Gabriel Hurtado, Albert Thomas
2021	J	`jnl`	Model-based micro-data reinforcement learning: what are the crucial model properties and which model to choose? CoRR Balázs Kégl, Gabriel Hurtado, Albert Thomas
2021	—	`conf`	OSNR prediction for optical links via learned noise figures. ECOC Sarah Kamel, Hartmut Hafermann, Dylan Le Gac, Ludovic Dos Santos, Balázs Kégl, Yann Frignac, Gabriel Charlet
2020	J	`jnl`	Tropical Cyclone Track Forecasting Using Fused Deep Learning From Aligned Reanalysis Data. Frontiers Big Data Sophie Giffard-Roisin, Mo Yang, Guillaume Charpiat, Christina Kumler-Bonfanti, Balázs Kégl, Claire Monteleoni
2019	J	`jnl`	InsectUp: Crowdsourcing Insect Observations to Assess Demographic Shifts and Improve Classification. CoRR Léonard Boussioux, Tomás Giro-Larraz, Charles Guille-Escuret, Mehdi Cherti, Balázs Kégl
2019	J	`jnl`	Tropical Cyclone Track Forecasting using Fused Deep Learning from Aligned Reanalysis Data. CoRR Sophie Giffard-Roisin, Mo Yang, Guillaume Charpiat, Christina Kumler-Bonfanti, Balázs Kégl, Claire Monteleoni
2018	J	`jnl`	Similarity encoding for learning with dirty categorical variables. CoRR Patricio Cerda, Gaël Varoquaux, Balázs Kégl
2018	J	`jnl`	Similarity encoding for learning with dirty categorical variables. Mach. Learn. Patricio Cerda, Gaël Varoquaux, Balázs Kégl
2018	J	`jnl`	Spurious samples in deep generative models: bug or feature? CoRR Balázs Kégl, Mehdi Cherti, Akin Kazakçi
2017	—	`conf`	De novo drug design with deep generative models : an empirical study. ICLR (Workshop) Mehdi Cherti, Balázs Kégl, Akin Kazakçi
2017	J	`jnl`	Machine learning for classification and quantification of monoclonal antibody preparations for cancer therapy. CoRR 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
2017	B	`conf`	Out-of-Class Novelty Generation : An Experimental Foundation. ICTAI Mehdi Cherti, Balázs Kégl, Akin Kazakçi
2017	—	`conf`	Out-of-class novelty generation: an experimental foundation. ICLR (Workshop) Mehdi Cherti, Balázs Kégl, Akin Kazakçi
2016	C	`conf`	Digits that are not: Generating new types through deep neural nets. ICCC Akin Kazakçi, Mehdi Cherti, Balázs Kégl
2016	J	`jnl`	Digits that are not: Generating new types through deep neural nets. CoRR Akin Kazakçi, Mehdi Cherti, Balázs Kégl
2016	B	`conf`	How machine learning won the Higgs boson challenge. ESANN Claire Adam-Bourdarios, Glen Cowan, Cécile Germain, Isabelle Guyon, Balázs Kégl, David Rousseau
2014	—	`conf`	Correlation-based construction of neighborhood and edge features. ICLR (Workshop Poster) Balázs Kégl
2014	A*	`conf`	Open Problem: A (missing) boosting-type convergence result for AdaBoost.MH with factorized multi-class classifiers. COLT Balázs Kégl
2014	—	`conf`	The Higgs boson machine learning challenge. HEPML@NIPS Claire Adam-Bourdarios, Glen Cowan, Cécile Germain, Isabelle Guyon, Balázs Kégl, David Rousseau
2014	—	`conf`	The return of AdaBoost.MH: multi-class Hamming trees. ICLR (Poster) Balázs Kégl
2013	—	`conf`	Collaborative hyperparameter tuning. ICML (2) Rémi Bardenet, Mátyás Brendel, Balázs Kégl, Michèle Sebag
2013	—	`conf`	Gossip-based distributed stochastic bandit algorithms. ICML (3) Balázs Szörényi, Róbert Busa-Fekete, István Hegedüs, Róbert Ormándi, Márk Jelasity, Balázs Kégl
2013	J	`jnl`	Tune and mix: learning to rank using ensembles of calibrated multi-class classifiers. Mach. Learn. Róbert Busa-Fekete, Balázs Kégl, Tamás Éltetö, György Szarvas
2012	A	`conf`	Adaptive Metropolis with Online Relabeling. AISTATS Rémi Bardenet, Olivier Cappé, Gersende Fort, Balázs Kégl
2012	A*	`conf`	Fast classification using sparse decision DAGs. ICML Róbert Busa-Fekete, Djalel Benbouzid, Balázs Kégl
2012	J	`jnl`	MULTIBOOST: A Multi-purpose Boosting Package. J. Mach. Learn. Res. Djalel Benbouzid, Róbert Busa-Fekete, Norman Casagrande, François-David Collin, Balázs Kégl
2012	—	`conf`	Peer-to-Peer Multi-class Boosting. Euro-Par István Hegedüs, Róbert Busa-Fekete, Róbert Ormándi, Márk Jelasity, Balázs Kégl
2011	—	`conf`	A Robust Ranking Methodology Based on Diverse Calibration of AdaBoost. ECML/PKDD (1) Róbert Busa-Fekete, Balázs Kégl, Tamás Éltetö, György Szarvas
2011	—	`conf`	Algorithms for Hyper-Parameter Optimization. NIPS James Bergstra, Rémi Bardenet, Yoshua Bengio, Balázs Kégl
2011	—	`book`	Contributions to machine learning: the unsupervised, the supervised, and the Bayesian. Balázs Kégl
2011	—	`conf`	Ranking by calibrated AdaBoost. Yahoo! Learning to Rank Challenge Róbert Busa-Fekete, Balázs Kégl, Tamás Éltetö, György Szarvas
2010	A*	`conf`	Fast boosting using adversarial bandits. ICML Róbert Busa-Fekete, Balázs Kégl
2010	J	`jnl`	Invariant pattern recognition using contourlets and AdaBoost. Pattern Recognit. Guangyi Chen, Balázs Kégl
2010	J	`jnl`	Multi-objective Reinforcement Learning for Responsive Grids. J. Grid Comput. Julien Perez, Cécile Germain-Renaud, Balázs Kégl, Charles Loomis
2010	—	`conf`	Palmprint Classification Using Wavelets and AdaBoost. ISNN (2) Guangyi Chen, Wei-Ping Zhu, Balázs Kégl, Róbert Busa-Fekete
2010	A*	`conf`	Surrogating the surrogate: accelerating Gaussian-process-based global optimization with a mixture cross-entropy algorithm. ICML Rémi Bardenet, Balázs Kégl
2009	—	`conf`	A One-Class Classification Approach for Protein Sequences and Structures. ISBRA András Bánhalmi, Róbert Busa-Fekete, Balázs Kégl
2009	—	`conf`	Accelerating AdaBoost using UCB. KDD Cup Róbert Busa-Fekete, Balázs Kégl
2009	A*	`conf`	Boosting products of base classifiers. ICML Balázs Kégl, Róbert Busa-Fekete
2008	B	`conf`	Grid Differentiated Services: A Reinforcement Learning Approach. CCGRID Julien Perez, Cécile Germain-Renaud, Balázs Kégl, Charles Loomis
2008	—	`conf`	Utility-Based Reinforcement Learning for Reactive Grids. ICAC Julien Perez, Cécile Germain-Renaud, Balázs Kégl, Charles Loomis
2007	B	`conf`	Feature extraction using Radon, wavelet and fourier transform. SMC Guangyi Chen, Balázs Kégl
2007	J	`jnl`	Image denoising with complex ridgelets. Pattern Recognit. Guangyi Chen, Balázs Kégl
2007	—	`conf`	Learning the 2-D Topology of Images. NIPS Nicolas Le Roux, Yoshua Bengio, Pascal Lamblin, Marc Joliveau, Balázs Kégl
2007	B	`conf`	Palmprint classification using contourlets. SMC Guangyi Chen, Balázs Kégl
2007	J	`jnl`	Privacy-preserving boosting. Data Min. Knowl. Discov. Sébastien Gambs, Balázs Kégl, Esma Aïmeur
2006	J	`jnl`	Aggregate features and ADABOOSTfor music classification. Mach. Learn. James Bergstra, Norman Casagrande, Dumitru Erhan, Douglas Eck, Balázs Kégl
2006	—	`conf`	Invariant Radon-Wavelet Packet Signatures for Pattern Recognition. CCECE Guangyi Chen, Balázs Kégl
2005	—	`ed.`	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 Canadian AI Balázs Kégl, Guy Lapalme
2005	—	`conf`	Frame-Level Audio Feature Extraction Using AdaBoost. ISMIR Norman Casagrande, Douglas Eck, Balázs Kégl
2005	—	`conf`	Geometry in sound: a speech/Music audio Classifier Inspired by an Image Classifier. ICMC Norman Casagrande, Douglas Eck, Balázs Kégl
2004	—	`conf`	Boosting on Manifolds: Adaptive Regularization of Base Classifiers. NIPS Balázs Kégl, Ligen Wang
2004	—	`conf`	Generalization Error and Algorithmic Convergence of Median Boosting. NIPS Balázs Kégl
2003	A*	`conf`	Robust Regression by Boosting the Median. COLT Balázs Kégl
2002	—	`conf`	Combining Software Quality Predictive Models: An Evolutionary Approach. ICSM Salah Bouktif, Houari A. Sahraoui, Balázs Kégl
2002	A*	`conf`	Combining and Adapting Software Quality Predictive Models by Genetic Algorithms. ASE Danielle Azar, Doina Precup, Salah Bouktif, Balázs Kégl, Houari A. Sahraoui
2002	J	`jnl`	Data-dependent margin-based generalization bounds for classification. J. Mach. Learn. Res. András Antos, Balázs Kégl, Tamás Linder, Gábor Lugosi
2002	—	`conf`	Intrinsic Dimension Estimation Using Packing Numbers. NIPS Balázs Kégl
2002	J	`jnl`	Piecewise Linear Skeletonization Using Principal Curves. IEEE Trans. Pattern Anal. Mach. Intell. Balázs Kégl, Adam Krzyzak
2001	—	`conf`	Data-Dependent Margin-Based Generalization Bounds for Classification. COLT/EuroCOLT Balázs Kégl, Tamás Linder, Gábor Lugosi
2001	—	`conf`	Identification of nonlinear systems by Hermite series approach. CDC Adam Krzyak, Jerzy Z. Sasiadek, Balázs Kégl
2001	J	`jnl`	Non-parametric identification of dynamic non-linear systems by a Hermite Series Approach. Int. J. Syst. Sci. Adam Krzyzak, Jerzy Z. Sasiadek, Balázs Kégl
2000	J	`jnl`	Learning and Design of Principal Curves. IEEE Trans. Pattern Anal. Mach. Intell. Balázs Kégl, Adam Krzyzak, Tamás Linder, Kenneth Zeger
2000	B	`conf`	Piecewise Linear Skeletonization Using Principal Curves. ICPR Balázs Kégl, Adam Krzyzak
2000	B	`conf`	Radial Basis Function Networks and Complexity Regularization in Function Learning and Classification. ICPR Balázs Kégl, Adam Krzyzak, Heinrich Niemann
1998	—	`conf`	A Polygonal Line Algorithm for Constructing Principal Curves. NIPS Balázs Kégl, Adam Krzyzak, Tamás Linder, Kenneth Zeger
1998	B	`conf`	Radial basis function networks in nonparametric classification and function learning. ICPR Balázs Kégl, Adam Krzyzak, Heinrich Niemann